This paper presents an architecture for automated fault recovery in special-purpose machinery using standard IEC 61131-3 PLC control software. Building upon the MAPE-K paradigm (Monitor, Analyze, Plan, Execute with Knowledge), the proposed three-level architecture integrates runtime control via a previously presented modularization concept called Control Primitives, high-level reasoning using Regionalized Resource Process Dependence Graphs (RRPDGs), and an intermediate Resource Agent that bridges field-level execution and recovery planning. The architecture supports fully automated dynamic recovery and seamless resumption of productive operations while maintaining real-time reliability. Experimental evaluation demonstrates the scalability and low resource demands of the planning system across simulated scenarios and validates the concept on a lab-sized real-world demonstrator. The architecture and interfaces build a modular foundation for future research and industrial applications.

Rapid technological advances in recent years have established Big Data, Industrie 4.0, Internet of Things (IoT) and Artificial Intelligence (AI) as data-driven concepts that are increasingly being implemented and proving value in real-world scenarios, transforming the future across all sectors—including manufacturing. Especially in the area of industrial automation, these fast-paced and transformative changes collide with a long-established industry. Despite the current state-of-the-art in technology, the lifetime of equipment is measured in decades— with the need to provide long-term support even to outdated (legacy) systems in heterogeneous production environments. Incorporating changes in such systems is challenging and mostly driven by software due to its adaptability. This paper contributes a generalized, lightweight approach for integrating machine-level data collection and analysis with agent-based systems, aiming to facilitate the adoption of CPPS in the industry and easing the integration with legacy control systems. As a use case for production performance optimization, the monitoring and detection of outliers for interconnected transportation systems has been defined. A key feature of the approach is that it utilizes only data from presence sensors. Thus, only minimal information about the system and no further configuration or development is required while maintaining full functionality. The generalizability and transferability of this approach have been validated using two representative laboratory production systems, demonstrating the potential of PLC-based data (pre-) processing and its scalability to multiple IEC 61131-3 conform control applications.

Automated Production Systems (aPS) in regulated industries such as pharmaceuticals, MedTech, or food and beverages must comply with the stringent validation and documentation requirements of Good Manufacturing Practice (GMP) regulations within the European Union (EU). These obligations create significant burdens for aPS manufacturers, particularly when changes require revalidation through manual integration and system testing. But these prerequisites also enable opportunities for lower-effort software verification, leveraging GMP documentation and boundaries of the automation domain to prove, e.g., that an implemented software change realizes the change specification without side effects. This paper proposes a methodical workflow for deriving software slices suitable for formal verification, while being aligned with automation engineering practices and GMP requirements. It defines assumptions for slice utility based on system modularity, interface expressiveness, and domain boundaries. The approach is validated through a real-world GMP-regulated change from a German MedTech aPS manufacturer, following GAMP 5 guidelines, demonstrating the utility for GMP-regulated aPS engineering and automatic verification of a sliced program segment.

The interdisciplinary design of intralogistics systems (ILS) involves engineers from various disciplines, resulting in the generation of discipline-specific model files with overlapping information. For instance, a conveyor system can be represented from various perspectives, such as 3D-CAD models that capture its geometric information and discrete-event simulation models that depict the system’s dynamic material flow performance. The growing demands for flexible reconfigurability and adaptability in intralogistics systems necessitate frequent updates to engineering models. However, these updates often result in potential model inconsistencies due to insufficient stakeholder communication. Detecting the impact of model changes and related inconsistencies is challenging in practice due to data heterogeneity and complex inter-model relations. To address these challenges, we propose an ontology-versioning approach that automates the identification of inconsistencies resulting from model changes. Our approach facilitates the integration of heterogeneous model data, enables database versioning, detects inconsistencies caused by model updates, and provides traceability for identified issues. The concept is evaluated utilizing models from a prototypical implementation on a lab-sized demonstrator.Note to Practitioners—In the industry, the current development of intralogistics systems often lacks automated synchronization of overlapping model information and consistent model interfaces, frequently leading to contradictions among the models. This has been identified as a significant source of errors in the design of both industrial and academic intralogistics systems, as revealed by a study involving intralogistics experts from different technical disciplines. Effectively managing model inconsistencies is crucial for project success, particularly when frequent model changes occur. A promising approach to tackle this issue is to systematically link model data from different disciplines, through which model inconsistencies caused by inadequate communication among engineers can be identified and prevented. However, in many cases, changes in different model versions and their resulting inconsistencies are not adequately considered. To address this issue, we propose a concept based on ontology versioning that allows for the generation, comparison, and analysis of different versions of an ontological model database. This concept automatically identifies model changes, assesses their impacts on other models, and provides information to assist engineers in problem-solving. The effectiveness of our approach is assessed through an evaluation of three representative change scenarios, simplified from real-world use cases. In future research, we plan to extend the approach to general production systems and incorporate industrial-scale models from the broad range of disciplines involved in the design process.

A Diaphragm Wall Hydraulic Grab (DWHG), used in civil engineering for bulk excavation, is featured with a cable-suspended end-effector (also named attachment tool). The end-effector begins to oscillate during DWHG operation. Essential insights into the DWHG’s performance or productivity in operation could be derived from end-effector oscillations. However, due to limited robustness, the end-effector oscillation curves from a DWHG in operation can not be tracked directly by motion sensors attached to the end-effector. This article presents and evaluates a novel approach for DWHGs to infer cable-suspended end-effector oscillations from their hydraulic actuators’ responses. Hydraulic actuators’ responses depict the changes of hydraulic parameters in hydraulic actuators due to end-effector oscillations. The main contribution of this article is an investigation of how far oscillations of a cable-suspended end-effector in a DWHG can be inferred from their hydraulic actuators’ responses. For this purpose, a workflow is conducted: firstly, end-effector oscillations and their hydraulic actuators’ responses are collected for a typical DWHG steering sequence. Secondly, the collected end-effector oscillation and hydraulic actuators’ response curves are analyzed and processed. Thirdly, a model to infer end-effector oscillations from hydraulic actuators’ responses is built. Fourthly, an evaluation shows that the inferred oscillation curves (model output) are similar to the true end-effector oscillationsNote to Practitioners—This article is motivated by the demand to determine the oscillation curves of the cable-suspended end-effector (also named attachment tool) at a Diaphragm Wall Hydraulic Grab (DWHG) used in civil engineering for bulk excavation. Existing approaches for construction machines commonly install motion sensors at the end-effector to track its oscillations. However, mounting any sensors at the end-effector in DWHGs is not practicable because the end-effector is exposed to mechanical shocks and moisture. Thus, mounted sensors would be damaged or demolished. This article suggests and investigates a novel approach for DWHGs to infer end-effector oscillations from pressure data in hydraulic actuators that actuate the end-effector. Therefore, the pressure curves at the hydraulic actuators are tracked while the end-effector oscillates and are utilized afterward to infer these oscillations. A practical guide for a DWHG is presented and evaluated to collect, analyze, process, and utilize pressure data from hydraulic actuators to infer end-effector oscillations.

In automated production, resilience describes a system’s capacity to absorb disturbances by reconfiguring itself, thus retaining its Overall Equipment Effectiveness at least partially. This includes online behavior reconfiguration to automatically recover from or prevent faults, collectively called fault handling. Promising research exists for fault handling in automated Production Systems. In process engineering, fault diagnosis and automatic parameter adaptions are already industrially available. However, handling faults in discrete manufacturing requires a series of distinct operations, which cannot be achieved by parameter changes alone. Further, core requirements must remain fulfilled by automatic fault handling approaches, including real-time control and extra-functional requirements like changing operation modes, monitoring interlocks, and an alarming and communication system. This article proposes a concept for reconfigurable IEC 61131–3 software for automatic fault handling, validated by a public reference implementation for a demonstrator, an industrial production system, and a modified industrial test rig. Eight experiments were successfully conducted, showcasing four use cases of the concept: The prevention of faults by avoiding anomalous components, the recovery from a fault state to automatic operation, the definition of previously undefined state variables, and the monitoring of global interlocks to trigger a controlled stop. All mentioned extra-functional requirements are fulfilled. Note to Practitioners—Identification, reporting, diagnosis, and recovery of faults in automated production incur substantial effort. Project-specific code is required for diagnosis, and the recovery and re-initialization are often performed manually. To our knowledge, automatic recovery approaches from scientific literature are not widely used in discrete manufacturing. Reasons may include a frequent disregard of extra-functional requirements mentioned above. Further, some approaches are incompatible with IEC 61131–3 or industry-typical software modularization. This article proposes a PLC software concept that aims to be compatible with real-world challenges and solutions. The functional software is vertically modularized from organizational hardware-level code. The horizontal modularization separates devices or equipment groups. Support for multiple changing operation modes including two types of controlled stop (run to completion or abort), alarming, data exchange, and global interlocks are incorporated. A prototypical IEC 61131–3 implementation is publicly available that separates a reusable generic part from hardware-specific and project-specific code. The resulting control code is highly reusable, such that all modes (derived from PackML), including dynamic reconfigurations, are composed from the same software modules. Note that we do not expect the concept to be well-adoptable in continuous processes, as elaborated in the Preliminaries section.

The design of manufacturing systems requires exploring diverse component configurations for one that best satisfies the requirements of the target system. At the same time, manufacturing systems evolve continuously, increasing their complexity and that of the production line in which they are installed. Model-based System Engineering (MBSE) methodologies and modeling languages like System Modeling Language (SysML) are used to assist system engineers during the entire design process. Yet, MBSE methodologies mainly rely on manually composed models for requirements verification, incurring enormous cost and effort especially in larger, more complex systems. This paper proposes a methodology exploiting the knowledge enclosed in a SysML model to automate the execution of a set of analyses defined over the represented manufacturing system. The proposed approach relies on Lingua Franca (LF) to simulate the specified analysis and analyze the results. Moreover, the methodology supports chains of analyses to represent dependent requirements and tradeoffs imposed upon a manufacturing system. The proposed methodology has been evaluated on a manufacturing system demonstrator. Results show the proposed methodology's applicability and scalability on diverse configurations of the manufacturing system.

Industrial alarm systems facilitate the reliable and effective management of industrial process operations. However, the manufacturer’s fear of missing a critical fault or lacking process understanding may lead to the implementation of alarm systems that overburden operators by an enormous amount of alarms. Inadequate alarm settings and maintenance, coupled with a high volume of disturbance alarms, necessitate operators making crucial decisions within a limited time. Thus, an alarm management strategy that accurately anticipates the types of incoming alarms is needed, especially in brownfield systems where it is infeasible to simplify the underlying alarming functions. This alleviates the need for human interpretation of alarms upon arrival, allowing operators to address anomalous behaviors early. This study proposes an alarm classification approach, based on active learning, latent dirichlet allocation (LDA) topic modeling and transformer-based deep learning. It focuses on addressing these challenges through a systematic natural language processing (NLP) approach to classify and map fault messages. To demonstrate the validity of this approach, real-world alarm data from an industrial hybrid non-woven manufacturing process is used.

Rolling element eccentric drives promise to be an easy-to-manufacture and performant gear system for robotic actuators. They share characteristics with other eccentric drives, such as strain wave and cycloidal drives, but use rolling elements instead of an eccentric gear. They offer reduced manufacturing complexity and costs by using readily available standard parts. Little research into rolling element eccentric drives is available, and their characteristics are still underexplored. This work uses a contact-based model to investigate the previously unknown stiffness of rolling element eccentric drives. Such calculation methods are well established for structurally similar components, such as cycloidal drives and roller bearings, and provide a high-level and computationally efficient model. Good stiffness models are critical for accurately predicting robotic actuator behavior and enabling better control of robotic systems. Additionally, the proposed model is used to calculate the contact forces under load occurring in rolling element eccentric drives. Contact forces are critical to calculating a drive’s load capacity, lifetime, and efficiency and serve as the foundation for further research. The mathematical description of the proposed model is derived, and the stiffness of a representative rolling element eccentric drive is calculated. Different manufacturing techniques, characterized by tolerance levels and material choices, are compared. Irrespective of manufacturing precision, similar stiffness curves result for drives made of steel, but higher contact forces result from less precise manufacturing. The stiffness of drives made from 3D printed plastic is considerably lower than that of drives made from steel. Additionally, the stiffness of rolling element eccentric drives is compared to similar eccentric drives, and a comparable twist-over-torque curve is shown.

The rising demand for customized products necessitates the integration of multiple robotic systems, underscoring the need for advanced production planning and scheduling. This paper introduces an ontology-based, artificial intelligence-enhanced method for dynamic task planning and scheduling, aimed at improving the efficiency of production process, reducing machine downtime, and consequently increasing throughput in assembly operations. Designed to generate and execute feasible production plans dynamically, this method minimizes manual planning and scheduling efforts. We evaluate its effectiveness using two gear assembly use cases with various robot skills, highlighting its flexibility in planning and scheduling and its contributions to the evolution of smart manufacturing. The method’s adaptability suggests its applicability across diverse smart factory environments.

Faults in the technical process of automated production systems (aPS) are challenging to detect, report, and recover accurately. Missing physical equipment, lacking detection mechanisms in the control software, and insufficient training and experience of human operators lead to an often delayed and imprecise detection of a fault's root cause. Since fault detection and recovery typically rely heavily on human involvement, there is also a risk of operators performing only partial repairs or inadvertently introducing new faults. Thereby, even slight deviations in human-machine interactions may affect process outcomes unpredictably, which so far cannot be modelled in field-level automation. The Functional Resonance Analysis Method (FRAM) is well-suited to address functional relations in such complex socio-technical systems and accurately represent resource requirements, preconditions, control, and time constraints, making it a promising tool to methodically investigate aPS faults. Yet, the FRAM has barely been applied in this domain. This paper thus introduces an approach relying on the FRAM to methodically derive alarm conditions and system extensions as a foundation for broader uses to investigate in future work. The FRAM-based approach was shown to reduce uncaught faults, required operator interventions, and thus unplanned downtime, in an experiment with two participants on a lab-sized demonstrator machine.

The usage of cloud applications in industrial automation domain is boosted by the desire to apply computational-intensive machine-learning (ML) models and access production data from all over the world. Accomplished by the demand for powerful hardware platforms for ML and centralized data storage, the connectivity performance also increases and allows high data transmission rates. Nevertheless, requirements for data transmission, like safety, security, and real-time must be considered in line with the production use case. Finding a suitable infrastructure with communication protocols for data transmission to transform legacy production plants into cyber-physical production systems (CPPS) is overwhelming due to the many available communication methods. Design support for CPPS would increase the acceptance of cloud-based solutions by engineers of automated production systems that are not specifically familiar with high-level information technology systems. This paper introduces an investigation and design recommendations for application-layer communication protocols usually used in industrial applications. The overall goal is to support the engineering for the different automation levels on field-, edge-, and cloud-level. In this paper, design measures are firstly derived from related work. Secondly, an analysis of the timing behavior and the CPU resources are carried out. Finally, the findings are collected in a summarizing rating table that briefly suggests adjusting the performance and the impact on the overall system design by selecting suitable communication protocols.

One of the limitations of classical SysML is that it does not provide information on how to model system component's constraints, which can result in individual model solutions for the same elements. To address this issue, we propose utilizing the reference mechanism in SysML to refer to REXS and ECLASS data standards, which can unambiguously define characteristic properties. Environmental factors such as temperature or humidity constraints play a vital role in designing components of mechatronic systems, like transmissions, or electrical and electronic components like sensors and actuators. Mechatronic component's properties may change depending on temperature and humidity and may show degradation. Such information is provided to some extent in the component's classified properties, like ECLASS and/or REXS, the engineering tool to configure gears. Such properties and constraints are further detailed in specification sheets provided by the manufacturer, e.g., by multi-dimensional manufacturer-specific parametric curves which are proposed to be explicitly included in the modeling process as SysML constraints. The proposed approach allows to include additional, not yet standardized environmental effects such as dirt layers in a formalized way. The formalized properties are modeled in parametric diagrams of SysML.

Intelligent manufacturing systems are often designed with decision-making ability and higher capabilities than required, referred to as capability redundancy. Considering the effects of degradation and wear, during operation system capability redundancy decreases gradually under uncertainty. This uncertainty needs to be considered to decide if the requirements are still satisfied after some years of operation. The proposed approach includes this uncertainty in the system model using SysML as modeling language and is represented through a metamodal. The metamodel encapsulates capability redundancy and multi-requirement satisfaction oriented decision-making under uncertainty. The metamodel can be used as knowledge base supporting intelligent decision-making during runtime as demonstrated in earlier work and thereby increasing the overall equipment effectiveness. A conveyor system is introduced as a case study.

Technical decisions that offer short-term gains but result in long-term disturbances and costs are often made due to the insufficient appreciation or underestimation of their scope, impact, and remedial actions. Technical Debt (TD) is a metaphor that embodies such phenomena and poses a particularly harmful threat when interdisciplinary teams interact and collaborate. The study presents new methods analyzing cross-company TD characteristics and positive TD best practice use cases gathered from 47 semi-structured expert interviews in the industrial automation domain. The three most important life cycle phases, the requirement, design, and testing phases, are addressed. The analysis demonstrates that, like adverse TD incidents, cross-life-cycle ripple effects can be advantageous or disadvantageous to the system. By implementing one measure, the system can benefit in multiple life-cycle phases and even disciplines. Additionally, the measures identified can prevent and eliminate numerous TD types and subtypes. The study elaborates on 31 measures that address 129 TD subtypes and proposes a systematic lessons-learned-based step for managing TD incidents in the automation sector.

The importance of high-performance computers in car networks increases to realize software assistance functions. These computers offer new opportunities for use cases in car testing and diagnostics towards vehicle software and services. Therefore, car testing and diagnostics are no longer limited to the testing and functional checking of electric and electronic components. Proven concepts from computer science, such as business process management, can be integrated into the car network to provide a uniform basis for automotive use cases. In this paper, (i) the Business Process Model and Notation (BPMN) standard is used to model diagnostic processes in future car production lines and (ii) a car-tailored BPMN workflow engine is introduced to execute the processes in a high-performance vehicular computer. The research on executable BPMN models in car networks bridges the gap between interdisciplinary business processes and execution in automotive IT systems.

Mobile and stationary mechatronic systems are often driven by hydraulic actuators which are controlled using valves. Hydraulic systems have a high power density, are robust and cost-effective in the application and can work reliably under rough environmental conditions. For many years, PID controllers build the state-of-the-technology for valve control. Although PID controllers are easy to synthesize and can be executed on low-performing devices, the demand for more adaptive control approaches to deal with non-linearities in hydraulic circuits is increasing. Adaptive controllers tend to be more computationally expensive than PID controllers, so the capability of common hardware devices to execute the algorithms in real-time must also be considered. This paper contributes a feasibility study examining whether adaptive control approaches can be used for valve control in hydraulic-driven mobile machines. Thus, a literature review on adaptive valve control approaches is conducted and a model-based linear adaptive control approach consisting of system identification and control logic execution is extracted. The adaptive controller is applied to a hydraulic testbed and the controller performance is measured in dependence on the cycle time of the controllers to estimate the worst-case execution time. It is investigated whether common control units in mobile machines can reach the execution times and how many valves can be controlled by one control unit in terms of the execution time.

Industry 4.0 requires high reconfigurability and flexibility of cyber-physical production systems (CPPS). Agent-based approaches are introduced to realize decentralized decision-making and flexibility within CPPS. Agents negotiate with each other regarding task allocation in production systems to achieve a global goal together. In non-deterministic systems, agents’ decision-making can become inaccurate due to misalignment between the agents’ beliefs and the actual state of the physical system they represent. (Un)Intentional misestimations can lead to non-optimal task allocation regarding the global system’s goal. Additionally, decisions that benefit individual agents’ goals, such as ‘get all tasks’, can contradict the global system’s goal. In this paper, a sanctioning approach known from socio-technical systems is integrated into a non-deterministic production plant consisting of a process part and a logistics part to increase the robustness of agents’ decision-making.

The concept of data-centered companies is of rapidly increasing interest within the process industry due to applications like data analysis, plant optimization and visualization. However, chemical companies face high barriers regarding the wide variety and heterogeneity of data along the brownfield plant lifecycle caused by the long history of tool-focused enterprises. This paper proposes an Information Model architecture for the process industry and an approach to overcome the hurdles associated with brownfield plants and information mapping.

Due to emerging data-driven approaches in factory automation in the course of Industry 4.0, automated Production Systems must incorporate additional algorithms for data collection and processing tasks. However, strict real-time requirements, resource constraint devices, such as Programmable Logic Controllers or low-power edge devices, and network bandwidth limitations pose a challenge to selecting suitable algorithms for specific edge devices and vice-versa, also known as software-hardware co-design. Measuring the execution time of an algorithm or code snippet is therefore a crucial part of algorithm and hardware assessment and is incorporated in numerous benchmarks. However, this is not trivial since most existing time measurement methods are designed with specific devices in mind with limited portability to different hardware platforms. Thus this paper provides an overview of the properties of selected execution time measurement methods to support their feasible deployment in edge computing, including legacy systems. A time measurement code snippet for Beckhoff Programmable Logic Controllers and recommendations for implementing software-based timing functions for heterogeneous devices help shorten development times. Besides, comparing execution time measurement methods highlights the challenges of creating a generic cross-platform benchmark in future research.

The axes of robots and robot-like systems (RLS) usually include e-motor-gearbox-arrangements for optimal connection of the elements. The characteristics of the drive system and thus also of the robot depend strongly on the gears. Different gearbox designs are available which differ in stiffness, efficiency and further properties. For an application-optimal design of RLS a uniform documentation and a comparability of gearbox concepts is a decisive factor. The application-optimal design is supported by an interdisciplinary approach between mechanical engineering and software design, guided by adequate product development methodology. The quite heterogeneous characterization of gearboxes for RLS which is currently the state of the art is a relevant obstacle in the flexible and optimal design of RLS. The paper shows the analysis of the gear structure with unified symbols for specific machine elements and contact types. The introduced method gives insight into the mechanical structure of the gearboxes. Similarities between gear types can thus be revealed. This also enables the classification of new developments in the state of the art. Moreover, the developed method for analyzing the gear structure can be used to determine the characteristics of gears. Examples for these characteristics are backlash, efficiency or stiffness. Specifically, the stiffness of gears can be synthesized by the force action of individual contacts and the individual phenomena that occur with them. The representation by individual phenomena also makes it possible to extend the calculation to include influencing parameters such as temperature that have not been sufficiently taken into account so far.

Creating digital representations of industrial facilities presents substantial opportunities for the industry. The development of digital twins opens up many prospective applications for industrial plants, such as facilitating virtual training environments, streamlining change management, and providing real-time understanding of remote scenes. However, creating and maintaining these digital twins involves complex tasks, including scanning industrial plants and annotating their subsystems. Many use cases necessitate specifying a Volume of Interest (VoI) for further processing while presenting severe environmental challenges for segmentation quality and worker safety. Besides that, widespread adoption also depends on creating methods that are easy to use, even for workers with limited 3D interaction knowledge. Currently, no commonly implemented method fulfills all these diverse requirements. This paper introduces an approach for specifying a VoI in industrial scenarios. Our approach defines a volume by intersecting the projection of hand-drawn surroundings on a small number of pictures of the target volume, utilizing Augmented Reality and Voxel Carving. It can successfully handle various sizes of target volumes and delivers an appropriately detailed result. Applying qualitative discussions and a quantitative evaluation, we ensure our application meets all requirements posed by the scenarios. This simple interaction metaphor, tailored to specific use cases, can serve as a versatile pattern for various digital twin scenarios. It offers a valuable alternative to 3D primitive-based segmentation methods.

Cars with software-intensive features such as autonomous driving bring new opportunities to Original Equipment Manufacturers (OEMs) and their manufacturing processes. The complexity of internal engineering processes is increasing as software-intensive functions are connected to electric and electronic car architectures, which need to be tested and enabled for functionality in car manufacturing. However, internal engineering processes can be simplified in car manufacturing by automating previously manual activities and flexibly managing variants. In this paper, we present a concept for managing the online scheduling of diagnostic tests during dynamic car manufacturing. We introduce the online scheduling of diagnostic tests on a high-performance car computer and compare the concept with state-of-the-art concepts for offline scheduling on remote production servers. To evaluate our concept, we conduct an OEM case study that shows that online scheduling not only reduces the total testing time in manufacturing while managing schedule variants more flexibly, but also increases the overall value added in car production by simplifying existing employee role models. We illustrate a scenario of how new car architectures can facilitate digital manufacturing processes.

Underlying and undiscovered technical debt (TD) that burdens the system and makes future changes more costly or impossible poses significant risks to mechatronic systems. Multi-disciplinary collaboration and cooperation lead to interdisciplinary interfaces and new life cycle phases that cause greater ripple effects to the TD distribution. When quantifying TD contagiousness in interdisciplinary engineering, only a few metrics, methods, or tools prove applicable. In this work, we propose a method containing two key metrics to quantify TD contagiousness across product life cycles and disciplines. Furthermore, we suggest a matrix multiplication method to quantify the adverse impact on each discipline and the system. By applying the methods to the data of three comparable companies in the industrial automation domain, the results enable us to measure and prioritize the TD incidents’ contagiousness. This method provides a first step towards the systematic quantification of TD in the interdisciplinary environment and provides metrics to compare systems based on objective factors.

Technical Debt (TD) is a significant concern in software development, particularly when interdisciplinary teams collaborate and interact. The goal of the study is to investigate TD causal chains and patterns in the industrial automation sector by analyzing 123 mechatronic TD incidents from 47 expert interviews across ten companies. Findings reveal that Requirements, Process, and Test TD are most common, while Build, Versioning, Manufacturing, Code, and Maintenance/Service TD are less frequent. Key causes include ”other priorities”, ”lack of time”, ”historically grown products”, ”lack of market analysis” and ”copy-paste-modify without revising tolerances.” The research identifies correlations between TD subtypes and causes/consequences in relation to company size, experts’ experience, and position, utilizing the Chi-square test and PrefixSpan algorithm. The study also maps the contagious character of TD using Neo4J graphical representation. This first in-depth analysis of TD causal chains in industrial automation contributes qualitatively to understanding TD patterns, helping researchers and practitioners assess TD contagiousness, comprehend its effects, prevent diffusion, and develop repayment strategies To the best of our knowledge, this study’s quantitative analysis approach provides the foundation that will enable future research identifying TD metrics and TD management in multidisciplinary engineering.

automated Production Systems (aPS) are highly complex mechatronic systems, partly operated for several decades. Particularly in the face of ever shorter innovation cycles in the context of Industry 4.0 and intensified global competition, it is essential for aPS manufacturers to develop high-quality, maintainable, and flexibly adaptable systems. Increasing the efficiency of aPS requires the implementation of sophisticated drive concepts that meet a variety of company-and process-specific requirements. However, the evaluation of design decisions of motion concepts in aPS and the challenges faced by industrial practitioners have so far barely been addressed in research. Therefore, this paper presents the results of a field study with six companies in the aPS manufacturing sector in Germany. The results shed light on the current state of industrial practice in motion systems, the challenges that are already solved well, and the issues that still show potential for improvement and, thus, serve as an outlook for future work in motion technology.

“Artificial Intelligence in Industry 4.0”, a technical report published by the working groups "Technological and Application Scenarios" and "Artificial Intelligence" (AI) of the Industry 4.0 (I4.0) platform, presents an innovative Industrial AI concept. Above all, it concludes that I4.0 experts and scientists must become accustomed to the behavior of autonomous AI-controlled systems, collaborate with them and comply with learnability requirements (predictability). Industrial AI instantly raises a set of concerns about existing norms and new standardizations. These frequently provide guidelines and, in some cases, offer procedures and implementations using design patterns. One way to produce AI in I4.0 systems is through Industrial Agents (IAs) due to their natural autonomy and additional intelligent characteristics, e.g., reactiveness, proactiveness, and human cooperativeness. Multi-Agent Systems (MASs) are particularly well suited for representing distributable AI that can develop I4.0 components being applied to various I4.0 scenarios. Considering the properties of IAs and the corresponding standards, an MAS architecture is used to understand the aspects of the flexible, intelligent, and automated Cyber-Physical Production System (CPPS). This article proposes a predictive IA for I4.0 (Agent4.0) to an agent-based CPPS architecture, leveraging IA design patterns and logical structure for implementing MAS. As a result, relevant standardized IA design patterns for I4.0 show how MAS can be created with the help of the Industrial AI requirements and Agent4.0 skills (functions) identified.

The amount of software and its development effort in machine and plant manufacturing is continuously increasing to currently up to 35-50% of the development personnel. However, achieving mature automation software that is adaptable to changed requirements during runtime for lifetimes of several decades still poses major challenges for machine and plant manufacturing companies. Previous industrial case studies show that companies deal with these challenges with different success. However, maturity levels are still missing to categorize and compare companies' automation software and development processes and, thus, enable a cross-company benchmark. Available approaches from computer science to categorize software maturity often do not consider the characteristics of automation software or only focus on individual aspects of maturity. Thus, this paper introduces the results of a large-scale questionnaire study with 61 German machine and plant manufacturing companies to enlarge an established maturity classification with quantitative and qualitative results on success factors in the design of automation software.

This IEEE Transactions on Automation Science and Engineering (T-ASE) Special Section on New Frontiers in Smart Factories: Smart Automation and Human–Robot Interaction focuses on promising, innovative research outcomes and industrial applications of different key technologies for smart automation and human–robot interaction.

Controller design and their software implementations are usually done in isolated design spaces using respective COTS design tools. However, this separation of concerns can lead to long debugging and integration phases. This is because assumptions made about the implementation platform during the design phase—e.g., related to timing—might not hold in practice, thereby leading to unacceptable control performance. In order to address this, several control/architecture co-design techniques have been proposed in the literature. However, their adoption in practice has been hampered by the lack of design flows using commercial tools. To the best of our knowledge, this is the first article that implements such a co-design method using commercially available design tools in an automotive setting, with the aim of minimally disrupting existing design flows practiced in the industry. The goal of such co-design is to jointly determine controller and platform parameters in order to avoid any design-implementation gap , thereby minimizing implementation time testing and debugging. Our setting involves distributed implementations of control algorithms on automotive electronic control units ( ECUs ) communicating via a FlexRay bus. The co-design and the associated toolchain Co-Flex jointly determines controller and FlexRay parameters (that impact signal delays) in order to optimize specified design metrics. Co-Flex seamlessly integrates the modeling and analysis of control systems in MATLAB/Simulink with platform modeling and configuration in SIMTOOLS/SIMTARGET that is used for configuring FlexRay bus parameters. It automates the generation of multiple Pareto-optimal design options with respect to the quality of control and the resource usage, that an engineer can choose from. In this article, we outline a step-by-step software development process based on Co-Flex tools for distributed control applications. While our exposition is automotive specific, this design flow can easily be extended to other domains.

automated Production Systems (aPS) are highly complex, mechatronic systems that usually have to operate reliably for many decades. Standardization and reuse of control software modules is a core prerequisite to achieve the required system quality in increasingly shorter development cycles. However, industrial case studies in aPS show that many aPS companies still struggle with strategically reusing software. This paper proposes a metric-based approach to objectively measure the m aturity of i ndustrial IEC 61131-based co ntrol s oftwar e in aPS (MICOSE4aPS) to identify potential weaknesses and quality issues hampering systematic reuse. Module developers in the machine and plant manufacturing industry can directly benefit as the metric calculation is integrated into the software engineering workflow. An in-depth industrial evaluation in a top-ranked machine manufacturing company in food packaging and an expert evaluation with different companies confirmed the benefit of efficiently managing the quality of control software.

Cyber-Physical Production Systems (CPPS) are long-living and variant-rich systems. Challenges and trends in the context of Industry 4.0 such as a high degree of customization, evolution, and different disciplines involved, e.g., mechanics, electrics/electronics and software, cause a high amount of variability. Mastering the variability of functional control software, e.g., different control variants of an actuator type, is itself a challenge in the development and reuse of CPPS software. This task becomes even more complex when considering the variability of human-machine interface (HMI) software and extra-functional software such as operating modes, diagnosis or fault handling. Moreover, the interdependencies between functional, extra-functional and HMI software pose an additional challenge for variability management and the planned reuse of these software parts. This paper illustrates the challenges in documenting the dependencies of these software parts including their variability using family models. Additionally, the current state-of-practice in industry, which was derived in questionnaires and interview studies, is shown and compared to the potential of increasing the software's reusability and thus its flexibility in the context of Industry 4.0 by concepts using the object-oriented extension of IEC 61131–3.

The automotive industry is increasingly focusing on product customization. The concept of Modular Production addresses this issue by providing more flexibility in production with Automated Guided Vehicles transporting products between modular workstations. The added complexity of Modular Production Control calls for approaches that can handle the scheduling complexity while also minimizing production costs. As a result, literature has focused on two promising approaches: Deep Reinforcement Learning and Multi-Agent Systems. Both approaches have their advantages. Especially in complex, large-scale production environments with random breakdowns, those two fields have been seldomly combined, though. As a result, this article aims to fill that research gap by introducing a Deep Reinforcement Learning Multi-Agent System approach for Modular Production Control. We introduce a reward design incentivizing agents to achieve maximal throughput. In addition, we show that the method learns optimal behavior even in a large-scale production environment with random machine breakdowns. Lastly, we compare the Multi-Agent System to a single-agent implementation of the Deep Reinforcement Learning approach and conclude that the Multi-Agent Deep Reinforcement Learning method learns and solves the Modular Production Control problem with the same solution quality as the single agent. Hence, the approach allows to foster MAS benefits such as robustness without losses in the solution quality.

Complexity of products, volatility in global markets, and the increasingly rapid pace of innovations may make it difficult to know how to approach challenging situations in mechatronic design and production. Technical Debt (TD) is a metaphor that describes the practical bargain of exchanging short-term benefits for long-term negative consequences. Oftentimes, the scope and impact of TD, as well as the cost of corrective measures, are underestimated. Especially for mechatronic teams in the mechanical, electrical, and software disciplines, the adverse interdisciplinary ripple effects of TD incidents are passed on throughout the life cycle. The analysis of the first comprehensive survey showed that not only do the TD types differ in cross-disciplinary comparisons, but different characteristics can also be observed depending on whether a discipline is studied in isolation or in combination with others. To validate the study results and to report on a general consciousness of TD in the disciplines, this follow-up study involves 15 of the 50 experts of the predecessor study and reflects the frequency and impact of technical debt in industrial experts' daily work using a questionnaire. These experts rate 14 TD types, 47 TD causes, and 33 TD symptoms in terms of their frequency and impact. Detailed analyses reveal consistent results for the most frequent TD types and causes, yet they show divergent characteristics in a profound exploration of discipline-specific phenomena. Thus, this study has the potential to set the foundations for future automated TD identification analyses in mechatronics.

Today’s mechatronic systems are becoming more and more complex as the development and design processes require collaboration across multiple engineering disciplines. The high complexity between loosely connected models results in a disconnect between these inter-disciplinary models, which may cause delays and costly rework in the long run. There is a need for an effective method to show the dependencies and risks across models for various stakeholder groups. Currently, the discussion about such methods is limited. This paper proposes and evaluates a user-friendly Decision Graph and Matrix Visualization method that combines network-based and matrix-based techniques to assist stakeholders in tracking dependencies and risks across models. The method is applied and demonstrated with a design and engineering case of a lab-scale production system, whose usability was tested formatively with experts.

Modern Cyber-Physical Production Systems get more and more intelligent by higher capacities of the used resources and more resource-efficient AI-algorithms. However, a significant challenge is finding the fitting architecture for hardware and software cost-efficiently and with low effort. Currently, this process consists of trial and error or selecting overpowered hardware resources, which leads to expensive and time-consuming processes in the development. This paper deals with a quantitative benchmark of the timing behavior of selected algorithms for preprocessing to enable AI on representative hardware platforms in cyber-physical production systems, building on previous approaches that take a model-based view of hardware/software co-design. This approach is a first step away from a purely qualitative system design, towards a quantitative approach.

automated Production Systems (aPS) represent a special type of mechatronic system characterized by a high complexity in hardware and software, lifetimes of up to several decades, and strong coupling of the involved disciplines, i.e., mechanics, electrics/electronics, and control software. Questionnaire studies have proven to be a valuable means of identifying obstacles in industrial software development that hinder the implementation of emerging technologies in the context of Industry 4.0 and, thus, the realization of Cyber-Physical Production Systems. However, the heterogeneity of different industrial sectors in terms of, e.g., the type of production processes or legal specifications, makes it difficult to compare the maturity of control software development across companies, even within the same industry. A software maturity benchmark for companies operating in similar industries is, however, a prerequisite for assessing a company’s strengths and weaknesses and thereby applying tailored solutions to improve the software and, thus, the competitiveness. This paper describes the results of a questionnaire study in two international companies operating in the same industrial sector. Specifically, location-, company-, and industry-specific maturity characteristics are analyzed and discussed, and it is concluded that the selected questionnaire is capable of identifying deviations and similarities in software maturity, even in an international environment, given that the companies are prefiltered based on the covered supply chain step. Hence, assuming a sufficiently large number of participants per industry, it enables a questionnaire-based industry comparison in future work, which was not possible until now.

In the automated Production Systems (aPS) domain, companies need to continuously decrease their systems’ development times while maintaining quality to stay globally competitive. Industry 4.0 imposes additional boundary conditions, e.g., a high degree of customization, that need to be met by aPS, which frequently have to be adapted to changing requirements during their long life cycles. Since control software implements an increasing share of aPS functionality, reusing well-tested software modules can significantly contribute to saving development time and increasing its comprehensibility and maintainability. However, planned reuse of control software modules still represents a major challenge in practice, e.g., the data exchange between software modules causes dependencies, which are often not directly visible and, thus, hinder reuse. This paper presents a metric-based concept for assessing the reusability of control software modules, focusing on different implementation types of indirect data exchange. Depending on company-specific boundary conditions, the approach can be integrated at various development process steps for continuous or one-time reuse assessment. The concept has been developed and evaluated with two industrial software projects and continuous feedback from domain experts.

Cyber Physical Production Systems (CPPS) operate for a long time and face continuous and incremental changes to follow up varying requirements. Interdisciplinary engineering of CPPS is often subject to delay and cost overrun; and quality control may even fail due to the lack of efficient information exchange between multiple involved actors. We propose to integrate a formal requirement specification approach, namely Generalized Test Tables including tool support, into industrial workflows and present the approach through extended notations of Business Process Model and Notation (BPMN), namely BPMN++*, with the tool-coupling aspect. The suggested tooling enables automation engineers to follow the defined workflow systematically and communicate easier through the formally represented change requirement. The approach is demonstrated by two typical use cases of changing a CPPS’ control software and showing the result by means of an extended BPMN++ model exemplarily.

The training of Deep Reinforcement Learning algorithms on robotic devices is challenging due to their large number of actuators and limited number of feasible action sequences. This paper addresses this challenge by extending and transferring existing approaches for waypoint-based exploration with Hierarchical Reinforcement Learning to the domain of robotic devices. The resulting algorithm utilizes a top-level policy, which suggests waypoints to a bottom-level policy that controls the system actuators. The waypoints can either be provided to the top-level policy as domain knowledge or be learned from scratch. The algorithm explicitly accounts for the low number of feasible waypoints and waypoint transitions that are characteristic of robotic devices. The effectiveness of the approach is evaluated on the simulation of a research demonstrator, and a separate ablation study proves the importance of its components.

Digitalization of products and production systems requires a fusion of mechatronic disciplines, where interfaces between mechanical, electrical, and software engineering are inevitable. The increasingly rapid pace of innovations in mechatronic systems triggers decisions being taken under time and cost pressure. At times, compromises in technical solutions are made, neglecting their long-term damage to the system. Technical debt (TD), a concept from software engineering, refers to short-term benefits that lead to long-term negative consequences, e.g., in the form of more difficult maintainability or evolvability. This also applies to mechatronic systems, yet the knowledge of TD characteristics and correlations in the interdisciplinary life cycle has only received little attention. This first comprehensive survey investigates TD in mechatronics systematically and across sectors. 50 experts, of whom 42% hold positions as department heads, from 21 renowned companies and 10 sectors in the German-speaking region supported this study with real scenarios where TD caused damage to their system. 94 informative TD incidents that were classified into twelve TD types were recorded, of which 2/3 have not yet been eliminated and posed a potential risk to the system. TD emerges most frequently in the first three stages of the life cycle, where the consequences rarely remain isolated at their source but are forwarded to later phases and disciplines in the life cycle. In contrast to the research focus in software engineering, the multi-domain analysis of mechatronic TD issues reveals that software engineers are most burdened by Requirements TD and Infrastructure TD in the interdisciplinary environment.

The first three industrial revolutions came about as a result of mechanization, electricity, and information technology (IT), respectively. Now, the introduction of the Internet of Things and Services into the manufacturing environment is fostering a 4th industrial revolution (Industry 4.0), where the smart optimization and computerization of all the actors and phases of the manufacturing process, including the conceptualization and design of a product, as well as its production and transaction, are taking a leading role.

Intelligent algorithms and learning are the basis for evolving smart cyber-physical production systems (CPPSs) and Industrie 4.0. A smart image detection algorithm shall be added to reduce downtime due to the extended operation of glass bottles in a yogurt producing plant. To support the engineers in doing so, a comprehensive domain-specific language (DSL), DSL4hDNCS, is introduced, enabling rapid analysis, addressing hardware/software architectures and network-related delays and uncertainties. DSL4hDNCS is defined by a metamodel to avoid ambiguity and enriched by aspects, such as safety, calculation power, and network transmission time. DSL4hDNCS is used to compare (re)deployment alternatives using different technologies, such as edge, fog, and cloud to implement the additional smart algorithm. The evaluation of DSL4hDNCS using an acknowledged Industrie 4.0 demonstrator plant as a case study confirmed the benefit for engineers during the redesign.

While modern production systems are becoming increasingly technologically advanced, the presence of human operators remains fundamental in industrial workplaces. To complement and enhance the capabilities of human workers, approaches based on adaptive automation have been introduced. They consist of adapting the behavior of the system according to the user’s capabilities and effort. In this article, we present a general holistic framework for adaptive automation, called INCLUSIVE, that assists the operator during working tasks. The system consists of three modules. First, a thorough characterization of the operator’s constitutional and situational condition is provided; based on this, properly tailored adaptation is given, and if necessary, further training and support are provided. The framework has been implemented and tested considering three industrial use cases, selected as representative of a wide area of interest for the industry in Europe, in terms of both production requirements and involved operators. Tests have been carried out in real production environments, considering real production tasks carried out by 53 shop-floor workers. Results have shown that workers’ satisfaction when using the INCLUSIVE system and their performances was increased with respect to customary interaction systems currently used in industries. Moreover, the achieved results were used to formulate a set of recommendations for the design and implementation of an adaptive interaction system in relation to ensuring worker satisfaction and system usability in an industrial environment, as well as performance requirements.Note to Practitioners—This article was motivated by the fact that, despite modern advanced automation, human operators are still central in the manufacturing process. However, technological progress often causes challenging interaction with complex industrial systems. The goal of this article is to introduce a complete framework for adaptive automation, with the ultimate goal of facilitating the interaction of human operators with complex industrial systems. The framework relies on three modules: measurement of human capabilities, the adaption of the interaction system, and additional teaching and support. The three modules are discussed at a high level, independently of the target application. Moreover, to facilitate their application in specific working contexts, examples are provided with respect to three different industrial applications. Results of tests carried out with shop-floor operators show that implementing the proposed framework allows better working performance and increases worker satisfaction with the use of automation.

Modern production systems are becoming more and more complex to comply with diversified market needs, flexible production, and competitiveness. Despite technological progress, the presence of human operators is still fundamental in production plants, since they have the important role of supervising and monitoring processes, by interacting with such complex machines. The complexity of machines implies an increased complexity of human-machine interfaces (HMIs), which are the main point of contact between the operator and the machine. Thus, HMIs cannot be considered anymore an accessory to the machine and their improvement has become an important part of the design of the whole machines, to enable a nonstressful interaction and make them easy to also use less skilled operators. In this article, we present a general framework for the design of HMIs that adapt to the skills and capabilities of the operator, with the ultimate aim of enabling a smooth and efficient interaction and improving user's situation awareness. Adaptation is achieved by considering three different levels: perception (i.e., how information is presented), cognition (i.e., what information is presented), and interaction (i.e., how interaction is enabled). For each level, general guidelines for adaptation are provided, thus defining a meta-HMI independent of the application. Finally, some examples of how the proposed adaptation patterns can be applied to the case of procedural and extraordinary maintenance tasks are presented.

Smart manufacturing is targeted as the next generation of manufacturing by many national and international strategic development. The increasingly rich production data, the integration and extensive application of information technology, and the intelligent data processing and system modeling methods have collectively enabled smart manufacturing. Building upon them, manufacturing system modeling, knowledge acquisition, design, and real-time control are the key components [item 1) in the Appendix], [item 2) in the Appendix]. It is still one of the really huge challenges to gather data, transform them into information, and derive knowledge out of this information, especially given the requirement of knowledge that can be trusted as manufacturing systems may harm humans and the environment if they come to the wrong conclusion. Despite the learning and derivation of knowledge, it could be modeled beforehand and taken, for example, as an environmental model for online decisions like in deliberative agent-based systems. Nevertheless, for decisions during operation, real-time requirements, dependability, and security issues are to be guaranteed. Finally, for acceptance and trust, humans need to “understand” the reasons behind automated decisions; therefore, explainability or at least a white box description is an issue of such knowledge-based systems.

A trend, accompanying the change of automation systems and their architectures, is the virtualization of software components. Virtualization strengthens platform-independent development and the provision of secure and isolated applications. Virtualization introduces well-defined interfaces to strengthen modularity, which facilitates the scalability of applications. However, virtualization includes additional software components and layers and, thus, additional computing costs. This additional effort can conflict with the real-time requirements of automation processes. Current research lacks the investigation of the time behavior of container-based virtualizations concerning their use in real-time systems. An assessment concerning real-time applications is required to prepare it for use in industrial automation. This article examines the effects of virtualization on the time delays of a software component based on Docker containers by providing measurements on a hardware testbed in a realistic use case. The experiments indicate that Docker virtualization can meet soft real-time requirements and can be used in industrial automation.

Machine learning and artificial intelligence provide methods and algorithms to take advantage of sensor and actuator data in automated production systems. Product quality monitoring is one of the promising applications available for data-driven modeling, particularly in cases where the quality parameters cannot be measured with reasonable effort. This is the case for defects such as cracks in workpieces of hydraulic metal powder presses. However, the variety of shapes produced at a powder press requires training of individual models based on a minimal sample size of unlabeled data to adapt to changing settings. Therefore, this article proposes an unsupervised product quality monitoring approach based on dynamic time warping and non-linear regression to detect anomalies in unlabeled sensor and actuator data. A preprocessing step that isolates only the relevant intervals of the process is further introduced, facilitating efficient product quality monitoring. The evaluation on an industrial dataset with 37 samples, generated in test runs, shows a true-positive rate for detected product quality defects of 100% while preserving an acceptable accuracy. Moreover, the approach achieves the output within less than 10 seconds, assuring that the result is available before the next workpiece is processed. In this way, efficient product quality management is possible, reducing time- and cost-intensive quality inspections.

Technical and interpersonal competences are essential characteristics that engineers need to obtain nowadays. Prior studies suggest that empathy and care are important skills in engineering within an increasingly global and innovative work environment. However, there is still limited literature about engineers‘ perceptions of empathy and care particularly when developing curricular approaches to strengthen engineers‘ empathic competences. Thereby, the current study aimed first to validate the factor structure of the US-American Empathy and Care Questionnaire (ECQ), by Hess, Strobel, Pan, and Wachter Morris (2017), regarding German engineers‘ perceptions of empathy and care at their workplaces. Second, the study sought to investigate the differences of perceived empathy of German engineers concerning engineers‘ years of work experience. Results suggest the five-factor model fit was acceptable and that German engineers with more professional experience perceived empathy and care as a more important aspect of their work in comparison to those with less professional experience.

Automated Production Systems (aPS) are highly complex, long-living mechatronic systems, which are usually programmed according to the standard IEC 61131-3. Control soft-ware development in aPS has to cope with challenges that sharply differ from classical high-level language programming, such as the assurance of maintainability for up to five decades or hard real-time requirements, e.g., to prevent damage to humans in case of a fault. In computer science, object-oriented programming is an established means to increase software reusability and maintainability. However, this approach is seldom used in IEC 61131-3-compliant control software, although the object-oriented extension of the standard (OO-IEC) has been available since 2013. Companies using OO-IEC generally benefit from a drastic reduction in the complexity of their control software. However, certain constellations of OO-IEC elements could lead to runtime problems, which is a crucial problem regarding the hard real-time requirements of aPS to guarantee safety. Therefore, this paper introduces two metrics to detect runtime-critical control software parts and optimization potentials without executing the code or performing time measurements. Using an industrial example, the metrics are evaluated with PLC experts of two companies, who confirmed the reliability and the advantages of the metrics.

The development of production systems involves engineers from different disciplines and different departments, who work simultaneously on different aspects or components of the product. Engineers use multiple models and development tools that generate input and output information. Some of these outputs are inputs for other design steps, especially for other departments, or are essential inputs for decision making. For large mechatronic systems, these different processes, tool chains and the input and output data form a complex dependency. Often these dependencies are also not explicitly known, which leads to inefficient and error-prone design processes. In this paper some problems of traditional development processes from the production systems domain and address various challenges Engineers have to cope with are highlighted. These include interdisciplinary, an ever increasing complexity, and time pressure. Based on this, an approach to modernise inefficient processes and relieve the engineer of additional, unnecessary work in examining and exchanging data is presented.

The availability of vast amounts of data in automated production systems reveals the potential for data-driven improvements. Jointly using this data across different sites or even across different companies will further increase the validity of data-driven models. However, the throughput in wide area networks is limited, limiting the large-scale transmission of data. Therefore, this paper proposes a data reduction approach to reduce network load based on regression and time series models directly on the shop floor. The machine-learning models are used to predict the signals of the automated production system to prevent the transmission of extensive raw data. It is shown that the approach reduces the network load significantly while still ensuring the fulfillment of the real-time control tasks of the programmable logic controller at any time. Thereby, the reduction of the data is dependent on the error of the reconstructed data that can be tolerated.

In an increasingly competitive market, short times to market and flexibility are key to success. We aim to shorten times to market by providing feasibility feedback to engineers early on in the design phase. Within the scope of this paper, we focus on intralogistics and assess feasibility based on the resources' layout and their properties. In addition to the feasibility feedback, intralogistics checks also result in potential production sequences. This additional information can be used to increase flexibility on the shop floor in case of unexpected changes during operation, e.g., a machine failing. Additionally, we provide a comparison of taxonomies of logistics resources, which we believe to be useful as a reference for the production domain.

Automated production systems (aPS) are variant-rich, design-to-order systems and an increasing proportion of their functionality is implemented by control software. In control software development, software reuse is still commonly performed via clone-and-own despite many drawbacks, e.g., copying errors. This unplanned reuse leads to a high amount of historically grown software variants, which contain valuable domain expertise. Therefore, to enable planned reuse of existing control software solutions, an analysis of legacy software, inducing documentation of identified variability, is required. While so-called Software Product Lines enable the documentation of variability, they lack suitable variability visualization tailored to the needs of aPS stakeholders such as application or module developers. To address this gap, this paper introduces a variability visualization concept tailored to the needs of aPS stakeholders with the aim of supporting them in their daily tasks. The concept was evaluated successfully within a master student's course by use of a prototypical implementation of the visualization concept.

Companies producing Robot-Like Systems (RLS) must increase efficiency in design and production in order to stay competitive in the international market. Such RLS range from small SCARA robots to entire production facilities. Very different systems for similar tasks are published in research or offered in the market. A clear path to an optimal configuration for a given task is apparently not available to the engineer. This indicates that the gear drives of such RLS and their integration with automation technology are still insufficiently researched, and their efficient design poses significant challenges for the industry. This paper identifies and provides a concise overview of requirements and challenges in the context of drives for such RLS. Building on this overview, suggestions are made for the future course of action in research.

The increasing demand for flexibility in automated production systems also affects the automated material flow systems (aMFSs) within these systems and, thus, demands reconfigurable systems. However, the centralized control concept usually applied in aMFSs hinders easy adaptation, as the entire control software has to be retested when subparts of the control are manually changed. As adaption and subsequent testing are time-consuming tasks, concepts are required for splitting the control from one centralized node to multiple, decentralized control nodes. Therefore, this article presents a holistic, agent-based control concept for aMFSs, whereby the system is divided into so-called automated material flow modules (aMFMs), each controlled by a dedicated module agent. The concept allows reconfiguring an aMFS consisting of heterogeneous, stationary aMFMs, during runtime. Furthermore, it includes aspects such as uniform agent knowledge bases through metamodel-based development, a communication ontology considering different information types and properties, strategic route optimization in decentralized control architectures, and a visualization concept to make decisions of the module agents comprehensible to operators and maintenance staff. We performed the concept evaluation using material flow simulations and a prototypical implementation on a lab-sized demonstrator. Note to Practitioners-Currently, the adaption of automated material flow systems (aMFSs) concerning their layout requires modifications to the control software, including extensive testing. This conflicts the demand for flexible and reconfigurable aMFSs due to changing requirements throughout a system's life cycle. A promising approach is the modularization of aMFSs, including their control to ease layout changes and adaptations to changing material flows (known as Plug and Produce in the scope of Industrie 4.0). However, common concerns when implementing agent-based control are the real-time capability of the controlled systems and the trust of operators in the automation regarding the safety and security of these systems. More precisely, it is feared that operators might be unable to distinguish the benevolent and malevolent behaviors of an aMFS if agents make decisions autonomously. The concept we present here addresses these issues by establishing different communication types classified according to varying real-time requirements, and by supporting operators via a human-machine interface to make agent decisions comprehensible.

Automated production systems (aPS) are often in operation for several decades. During their lifetime they undergo various changes and modifications due to the varying customer requirement, market or technology environment. In order to keep the system development and maintenance process under control, it is extremely important to be able to assess the effect of a change on the system and its cost. This paper introduce an approach for cost estimation of change occurring to the aPS using process, product and resources viewpoint on the system together with the capability of the resources. The estimation of the cost is supported by reusing the previous changes cost and effort that are collected in a knowledge base. The approach is evaluated using a lab size industrial demonstrator.

For modeling Networked Control System (NCS) a standardized notation is still missing. Approaches like the Unified Modeling Language (UML) or the Systems Modeling Language (SysML) are widespread and accepted, yet remain very general and do not offer support for domain specific problems. Additionally, UML/SysML do not offer graphical support to model domain specific solutions e.g. for network architectures in NCS. UML and SysML-based approaches like MARTE, TURTLE, UML-QoS or SysML-Sec provide more guidance to model network architectures, embedded systems and network systems. However, they still lack a well-defined graphical notation, also referred as concrete syntax, to support the modeling process. To strengthen the modelling, an existing graphical notation is extended, which enables traceability to requirements and the modelling of safety-relevant properties. As case study, a train network architecture is provided to show the feasibility of this notation. Together with industrial experts, the communication architecture of a train-part is modeled and linked to its requirements. The graphical representation includes the necessary information to define the communication in the NCS and is understandable for engineers of the domain without UML/SysML knowledge. The approach was evaluated with experts from industry.

The fourth industrial revolution, Industrie 4.0, motivates research to adapt concepts from Internet of Things and Cyber-Physical Systems to automation. Focus is placed on integrating all data sources, from sensors and actuators to management software, to achieve interoperability of heterogeneous systems. To assure data integration, a data collection architecture that guarantees data access from all systems, including legacy devices, is needed. This allows companies to gradually evolve to Industrie 4.0 deployments while still keeping their mission-critical systems online. A middleware-based architecture is a valid answer to the presented challenges. Numerous communication technologies can act as a middleware, all with different advantages and disadvantages. A technology-independent implementation ensures that a lock-in to a specific technology is prevented and makes the architecture more robust and adequate for various use-cases. This work contributes with a concept of a middleware-based data collection architecture, which is independent of specific communication protocols. Therefore, a protocol-agnostic communication interface is developed. A technology comparison summarizes relevant protocols in the field of industrial automation. The concept is subsequently implemented for a case-study and compared to a peer-to-peer legacy approach. Results show that the concept is feasible, as well as that it reduces complexity and effort for implementation and migration of such communication systems. Future work should focus on further developing the concepts, in order to support additional communication protocols and verify the suitability of the architecture in scenarios with a multitude of technologies.

The development of automated Production Systems (aPS) is an interdisciplinary process, where an increasing part of the system's functionality is realized in the respective control software. Such software projects commonly utilize programming languages standardized in IEC 61131-3. To measure, improve, and maintain source code while also promoting trust in its capabilities, an objective assessment of its characteristics is necessary. Software metrics are a means for such an evaluation. While there is an abundance of metrics available from the classical software engineering domain, these metrics focus on textual programming languages. IEC 61131-3, however, defines graphical languages, which are not targeted by renowned concepts in computer science. Besides, former research demonstrates that software engineering metrics for textual languages need adaption to be applicable in the aPS domain. Thus, this paper introduces a metrics suite consisting of adapted and newly developed measures, which focus on the graphical IEC 61131-3 language Function Block Diagram. The results are prototypically implemented in one of the leading integrated development environments for IEC 61131-3 and then evaluated regarding their understandability and applicability by practitioners at a German aPS manufacturer.

Modern large scale technical systems often face iterative changes on their behaviours with the requirement of validated quality which is not easy to achieve completely with traditional testing. Regression verification is a powerful tool for the formal correctness analysis of software-driven systems. By proving that a new revision of the software behaves similarly as the original version of the software, some of the trust that the old software and system had earned during the validation processes or operation histories can be inherited to the new revision. This trust inheritance by the formal analysis relies on a number of implicit assumptions which are not self-evident but easy to miss, and may lead to a false sense of safety induced by a misunderstood regression verification processes. This paper aims at pointing out hidden, implicit assumptions of regression verification in the context of cyber-physical systems by making them explicit using practical examples. The explicit trust inheritance analysis would clarify for the engineers to understand the extent of the trust that regression verification provides and consequently facilitate them to utilize this formal technique for the system validation.

In order to guarantee a high availability and reliability, automated production systems are tested thoroughly after changes were introduced. As test resources like testing time are limited, the tester has to select the test cases for the test execution thoughtfully. This becomes quite challenging as the complexity of automated production systems and the number of related test cases increases. It results in a high risk to miss important test cases and to waste valuable test resources by executing redundant or less important test cases. Automatic test case prioritisation can assist the tester in distinguishing between important and less important test cases. However to achieve an efficient test execution schedule, the test case selection as well as the running order have to be well conceived. Especially for automated production systems, there are many constraints and challenges to consider such as manual testing, setup time between test cases and effects upon test case failure. Therefore, an approach for an efficient test scheduling method is proposed in the paper that utilises the available resources as best as possible while maximizing the amount of the resulting information. The proposed test scheduling method hereby considers effects caused by the mechatronic characteristics of automated production systems, especially handling test case failure during the test run.

Integration of older employees into the workforce is critical for a successful manufacturing industry due to demographic change. However, technological developments to create more flexible manufacturing environments are leading to increasingly complex machinery that has to be operated by an aging workforce. Virtual training systems prepare operators for the interaction with industrial machines. Current virtual training systems do not address the perceptive and cognitive abilities of older users to enable a satisfying and efficient training. This article develops adaptations of the visualization and the interaction techniques of a virtual training system to address the abilities of older operators. The results of a between-subjects study indicate that the adaptations improve the subjective perception of the training system and decrease the training time. The paper concludes that adaptive training systems can support the participation of diverse user groups in the manufacturing industry by providing more effective and satisfying training.

The integration of Network Intrusion Detection Systems (Network IDS) in industrial networks has improved the security of these systems due to their ability to analyze network traffic in order to detect potential system intrusions. Unfortunately, their detection scope is often limited to strategical network locations and may therefore not be capable to detect intrusions occurring at other system locations (e.g., specific devices). Hence, it is necessary to increase their detection scope by further analyzing additional information pertaining to other system components. The introduction of these new information sources adds more complexity to the intrusion detection problem, as it is not only necessary to identify them, but it is also required to define how their authentication, capture and analysis is to be carried out. Multi-Agent Systems are an architectural paradigm that can deal with such complexity. This paper presents a multi-agent approach for hybrid intrusion detection that takes into consideration the aforementioned challenges. This approach is comprised of a multi-agent hybrid intrusion detection architecture designed according to a set of properties. These properties consider IDS-specific requirements. It also takes into consideration current trends in the field of Multi-Agent Systems to provide security, scalability and adaptability across multiple systems. The feasibility of this approach is validated through a prototypical implementation.

Engineers from the production systems domain have to cope with various challenges. These include interdisciplinarity, an ever increasing complexity, and time pressure. Due to these factors, engineers oftentimes choose suboptimal solutions, thus causing so-called Technical Debt (TD). No matter whether this TD is taken consciously or unconsciously, engineers need to become aware of their decisions' potential repercussions as early as possible. This paper presents a conceptual framework that helps engineers to identify conformance violations and inconsistencies based on formal knowledge bases. This increases awareness for potential TD. We hereby emphasize the importance of interdisciplinary inconsistencies, which are especially hard to keep track of. The framework also aims to assess the criticality of TD and thus supports decision making in the engineering of production systems.

The control software of large scale industrial systems such as machines and plants in the domain of automated Production Systems (aPS) is oftentimes developed using the method clone and own. However, cloning is one of the reason for high maintenance cost in software lifecycle and thus, the need for clone detection is arising. In large scale control software, clone detection is a tedious work, which cannot be performed manually. In order to alleviate the problem of huge number of clones in control software, structural clone detection can be performed. Detecting structural clones can help in better understanding of large scale and complex software, detecting commonly-used design patterns, and software evolution. In this work, the software structure is represented as a call graph depicting software artefacts and their direct dependencies. These call graphs are compared based on graph mining approach to detect similarities between two software structures. The proposed method is adapted and applied to two industrial use cases with different size and complexity. The obtained similar fragments in the software structures are evaluated and verified through manual analysis. The results show that the proposed method is promising approach to capture the similarities between two software structures.

The automation pyramid is shifting and will most likely not have a stock in rigid form. The shift is towards Cyber-Physical Systems (CPS) and Cyber-Physical Production Systems (CPPS). Some components in CPPS have to meet real-time requirements. One trend coming along with the change in automation architectures is the virtualization of applications. This enables platform independent development and deployment of secure and isolated applications. Yet, virtualization of applications comes with additional software components and therefore, additional processing effort. For web-services, virtualization is widespread, for embedded systems virtualization development and deployment is not common due time constraints of applications. The use of virtualized applications in embedded systems would have significant benefits like less effort in cross-platform development and cross compiling to state two examples. The method of virtualization in CPPS have to be evaluated concerning time-sensitive applications. In this approach, the time delays occurring through the additional virtualization layer with Docker containers are explored.

Successful innovation processes require communication between different disciplines, such as mechanical and electrical engineering or software development. However, such communication is hampered by differences in individual mental models. This paper presents two studies that investigate mental models of engineering students. The studies apply SA/RT and card sorting to analyze mental models and were conducted at the beginning of their academic education to understand influences on discipline-specific mental models that are observed in practice. The studies indicate that mental models of engineering students are typically based on structural properties of the machines. A second study identified nine dimensions that classify mental models of students. Future work that facilitates the analysis of mental models on a larger scale is proposed. The aim of this work is to understand these differences and inform educational mechanisms that teach mental model flexibility and perspective change.

The availability of huge amounts of process data enables data-driven methods to optimize production processes. Predictive Maintenance is one of the common applications to transfer data to useful information for improving the Overall Equipment Effectiveness. In this paper, a data-driven method for condition monitoring of control valves in industrial process plants is developed based on data collected during test runs. In contrast to the state of the art condition monitoring in control valves, the test runs make it possible to define a threshold that differentiates normal from abnormal valve behaviour. Furthermore, the characteristics of the model results allow the identification of different defects. The application of the proposed method to a historic industrial data set validate the applicability in noisy industrial use cases.

Industry 4.0 is estimated to have an economic potential of 267 billion euros for Germany. As a highly dynamic and innovative industry, electronics manufacturing is expected to contribute 23 billion euros. In order to realize this estimated potential, industry 4.0 technologies need to be implemented in concrete use cases providing value to electronics manufacturers. To better understand the current state of industry 4.0 use case adoption, we conduct expert interviews with eight electronics manufacturing companies from industrialized countries. With the results of the interviews, we aim to better understand, which industry 4.0 use cases provide the most value to electronics manufacturers and to what extent they are already implemented.

Industrial automated production systems are mechatronic and long living systems that undergo changing requirements throughout their life cycle. While the proportion of functionality implemented by software is growing, adjustments are usually implemented using a clone-and-own principle, which results in unmanaged software variants and versions. Furthermore, the need for adapting the control software also results from changes in other disciplines such as mechanical or electrical/electronics. The various drawbacks on software maintainability, that are provoked through clone-andown, call for a shift to modular development. As a first step to realize this migration, software projects need to be analyzed in terms of variability. Secondly, visualization patterns reflecting variability are needed to present the analysis results to domain experts. However, choosing an appropriate visualization is challenging as different domain experts pursue different aims, which should be supported by the visualization and might even require different levels of detail. In this paper, visualization patterns for three different use scenarios are proposed and evaluated using an apprentice group and industrial expert feedback.

Automated production systems become more and more complex. This makes it increasingly difficult to keep track of performed changes and already executed test cases. This endangers the systems quality as the risk of missing important test cases while planning the test execution is high, especially for testers with little experience. To face this challenge, testers should be supported by an automatic test prioritisation based on metrics in selecting the right test cases for the test execution. In industry, many different test prioritisation criteria and strategies are used for this purpose. In an industrial interview, experts discussed and ranked prioritisation criteria that are currently used within the respective companies. As a result, this paper presents the cactus prioritisation model, which graphically resembles the industrial ranking and weighting of the criteria. Based on the prioritisation cactus and its criteria, a simple prioritisation metric is introduced to determine the utility of each test case regarding the system under test. The test cases are prioritised according to their descending utility. Furthermore, approaches and metrics to realise the different individual prioritisation criteria are proposed.

Due to the increasing integration of different disciplines, Model-based Systems Engineering (MBSE) is becoming more beneficial for the development of mechatronic systems. However, even though all relevant discipline-specific models can be integrated into one system model, how to maintain consistency between a discipline-specific model and the central system model during model updates still needs to be considered. In this paper, an approach for automatic synchronization of a discipline-specific model i.e. mechanical CAD model and the central SysML-based model i.e. a SysML4 Mechatronics plant model, is introduced. AutomationML(AML), a data exchange format which enables a lossless exchange of information between different discipline-specific engineering tools, is applied during the model synchronization. Mapping rules are proposed to facilitate a consistent round-trip engineering and transformation. Feasibility and applicability of this approach are demonstrated through a use case on a production plant with two applied commercial tools, PTC Integrity Modeler and Creo Parametric.

Software product line engineering (SPLE) provides a systematic approach to manage variants and versions arising throughout the development of software systems. While SPLE is successfully applied for variant management in the domain of software engineering, the approach is still not widely spread in industrial automated production systems (aPS). Previous studies highlight the interdisciplinary nature of aPS as a reason for not applying SPLE, since control software variants and versions also result from changes in other disciplines such as the mechanical engineering department (i.e. exchange of a sensor). Additionally, the software may evolve over decades at the customer site. In order to gain a better understanding of the challenges in the development of aPS and the constraints hindering the use of SPLE, we conducted several interviews with software development engineers from the domain of aPS. The interviews main aim was to get an overview of the current state of variability management and applied planned and unplanned software reuse strategies. Based on these insights, we summarize the main results useable for a transition from currently deployed variability management concepts in aPS to the SPLE approach.

As long-living systems, automated Production Systems (aPS) have to be adapted due to optimization and inclusion of new features in their life cycle over decades. aPS consist of electrical, mechanical, and software components, which have a complex interaction and mutual dependencies. Consequently, these heterogeneous components have to be maintained together. Thus, the change propagation analysis in aPS is a challenging task. Existing approaches to change impact analysis lack tool-support and require expert knowledge in the aPS, as well as in the machine under study and its environment. This paper presents a tool-supported approach to change propagation analysis in aPS based on initial change requests. Our approach calculates a list of maintainability tasks to implement change requests in control programs deployed on Programmable Logic Controllers (PLC). To evaluate the quality and coverage of the generated task lists, we applied our approach to a community case study.

The engineering of automated Production Systems (aPS) involves various disciplines (i.e. mechanical, electrical and software engineers). In order to address discipline-specific objectives in the engineering process, a multitude of models are developed. The information within these models is represented by different variants of engineering documents. For instance, the information contained in an ECAD model can be stored in XML-based documents or can be represented by printed, paper-based electric circuit diagrams. Models and corresponding documents change cyclically, as the engineering process of an aPS is generally not straightforward. Consequently, changes made to those documents need to be propagated back to their original model, so as to maintain consistency. Depending on the meaning and the importance of the change, further action may be required (e.g. re-design of components). This paper proposes and evaluates the retrieval and model-based reconstruction of information from paper-based electric circuit diagrams in aPS, containing handwritten annotations.

Increasing integration of different disciplines such as mechanics, electrics/electronics and control software has led to higher complexity in the development of an automated production system (aPS), bringing forth some challenges along with it. In order to address these challenges, a multi-disciplinary engineering workflow can be built using a model-based system engineering (MBSE) approach. However, the various tools in different disciplines impede the efficient collaboration of different models. Because of the implicit dependencies between different disciplines, it is difficult to ensure the consistency after a change. This paper provides an approach to building an efficient multi-disciplinary engineering workflow based on MBSE. Through a combination of SysML4Mechatronics modeling language with inconsistency management and Product Lifecycle Management (PLM), the proposed engineering workflow facilitates the design of multi-disciplinary aPS and supports information tracking, consistency assurance, as well as change analysis across disciplines. The benefits of the proposed approach are demonstrated with a case study on a lab-sized production plant.

Multi-Agent Systems (MAS) are an implementation paradigm frequently discussed to be used within control system design for flexible production systems. Over the years, on the one hand different kind of MAS-based control architectures, and on the other hand, different agent system design methodologies have been developed with different intentions and focus e.g. Agent-Oriented Software Engineering (AOSE). Nevertheless, only a few practical applications of MAS in control can be observed. In fact, a MAS developers' problem is the missed availability of design support for agent-based engineering, to control complex production systems. Therefore, this paper intends to highlight a path towards such a MAS development support exploiting design patterns.

Internet technologies are driving developments in networking technologies for mechatronic devices. In the plant and manufacturing automation domain, internet technologies support for example cloud-based data exchange and a seamless connection of the Enterprise Resource Planning (ERP) and Manufacturing Execution System (MES). The communication of sensor-actuator loops on the field level is tackled with fieldbus technologies. Fieldbuses were introduced to tackle requirements such as real-time, safety, reliability and low costs that could hardly be met with Ethernet-based communication. Therefore, a variety of proprietary fieldbuses exists for the communication at the field level. For data exchange between different fieldbuses or for data upstream to higher automation layers, gateways to process data with significant time delays are mandatory. To overcome the shortcomings of Ethernet on the field level different approaches were proposed, labeled as Industrial Ethernet or Real-Time Ethernet (RTE). With Ethernet-based communication technologies, a vertical and horizontal integration of data is supported. While in simple or homogeneous networks, the estimation of Quality of Service (QoS) via simulation or calculation is possible; In complex architectures with heterogeneous protocols, different network layers, switches and gateways measurements are required. In this approach, a testing environment is presented to analyze different Ethernet-based technologies implemented on the single board computer i.e. Raspberry Pi. Therefore, measures for software configuration and a methodic analysis workflow are presented. This is beneficial to estimate QoS in planned communication architectures including different Ethernet-based protocols and gateways after defining requirements, functions and test scenarios.

Knowledge on the operating conditions of chemical processes is important for various applications, for instance failure diagnosis. In absence of the recorded information, it has to be reconstructed from other data. This is commonly carried out by process experts as the identification of the operating states requires a deep understanding of the underlying process. As manual classification is a time-consuming task, an automatic identification can simplify this task greatly. As data-driven approaches often fail due to the complex nature of the processes, two hybrid approaches are proposed in this paper, supporting the expert during the classification. Big data methods are used for processing with experts having the chance to influence and evaluate the algorithms and results. For identification, a k-Means clustering and a combination of self-organizing maps and hidden Markov models are used. While minimizing the expert effort for classification, the results still have to be reliable. Both approaches performed accurate and decreased the expert effort significantly. Future studies are centered on combining both methods as their strengths complement each other.

Technical debt (TD) describes the long-term negative effect of choosing sub-optimal solutions to achieve a short-term benefit (e.g., cost saving). Engineers from various disciplines, such as mechanical, electrical and software, develop and maintain automated production systems (aPS). Over their development and maintenance period, changes in one discipline might have negative or unexpected impact on the other disciplines due to the interwoven dependencies among the disciplines. Manually derived task list might omit some necessary tasks and, therefore, TD is quite often introduced unintentionally. Also, the interest of TD due to insufficient knowledge sometimes exceeds the short-term cost saving. In this paper, a workflow is proposed to assist in the solution selection process for the aPS domain. The workflow includes the systematic change effort estimation tool considering multidiscipline and TD interest estimation. KAMP4aPS is selected as a tool to estimate the change effort by deriving a fine-grained task list for a modification. Based on such detailed estimation and additional cost factors, one can foresee TD and choose appropriate solution, which fits the current business context. An exemplary change scenario in a lab size plant is presented to demonstrate the suggested methodology.

Estimation of missing values is an essential step in data pre-processing to increase the data quality for further data mining approaches. The significance of estimation of missing values in industrial data sets is that different operational situations cannot be describe properly while data sets includes missing values. In this paper, Expectation Conditional Maximization is used to find an approximated model over the data based on Gaussian distribution. Then, in the Expectation step, Sweep operation is used to obtain the regression model of missing values on observable values and estimate the missing values based on observable data. In order to evaluate the results a process data set for a real industrial production system is considered. The missing values are simulated by randomly removing the data from variables. Finally, the accuracy of the proposed method in estimation of missing values is discussed as well as the effect of imputation of missing values on further data analysis.

Pattern detection in alarm data aroused great interest in research activities in recent years. Reducing alarm floods, which arise of causal dependencies of equipment and their alarms in automated production systems, is aimed to decrease alarm rates and aggregate information to valuable notifications for the operator. However, common alarm flood analysis often lacks robustness against random occurring alarms or interfering alarm patterns, which disturb the known structure of sequences. Therefore, this contribution introduce a preprocessing step, calculating the dependencies of alarms probabilistically. This approach meet the fuzziness of alarm systems regarding precision in time domain and interfering alarm signals. The results, based on two different industrial data sets, reveal high accurateness of the clusters defined by the proposed method. Alarm patterns can be detected even though the sequences are interrupted and interfered by further alarms or further causal dependent alarm floods.

Adaptive and flexible production systems require modular, reusable software as a prerequisite for their long-term life cycle of up to 50 years. We introduce a benchmark process to measure software maturity for industrial control software of automated production systems.

When changes are performed on an automated production system (aPS), new faults can be accidentally introduced into the system, which are called regressions. A common method for finding these faults is regression testing. In most cases, this regression testing process is performed under high time pressure and onsite in a very uncomfortable environment. Until now, there has been no automated support for finding and prioritizing system test cases regarding the fully integrated aPS that are suitable for finding regressions. Thus, the testing technician has to rely on personal intuition and experience, possibly choosing an inappropriate order of test cases, finding regressions at a very late stage of the test run. Using a suitable prioritization, this iterative process of finding and fixing regressions can be streamlined and a lot of time can be saved by executing test cases likely to identify new regressions earlier. Thus, an approach is presented in this paper that uses previously acquired runtime data from past test executions and performs a change identification and impact analysis to prioritize test cases that have a high probability to unveil regressions caused by side effects of a system change. The approach was developed in cooperation with reputable industrial partners active in the field of aPS engineering, ensuring a development in line with industrial requirements. An industrial case study and an expert evaluation were performed, showing promising results.

The main feature of cyber-physical production systems is its adaptability. They adapt quickly to new requirements such as new products or product variants. Nowadays, a bottleneck is the automation system, for which high manual engineering efforts are needed: Today, on-site technicians write and rewrite automation software, configure real-time communication protocols and create system configurations consisting of machine timing, physical dimensions of products, sensitivity, and motor control accelerations and velocities. Cyber-physical production systems often solve this dilemma by relying on reusable software components, which are composed in the overall automation software. However, this solution comes with a price, reusable software components need free parameters to adjust to the individual production configurations. This paper addresses this central research question and presents a novel parameter estimation approach to choose automatically optimal system configurations for cyber-physical production systems. Different scenarios from discrete manufacturing plants are used to evaluate the solution approach.

Modern automotive systems consist of hundreds of functionalities implemented in software. Moreover, these functionalities are constantly evolving with increasing demand for automation, industry competition and changing sensor and actuator capabilities. Correspondingly, it is important to adapt the engineering and software development processes for such systems to consider fast management of this evolution at minimum cost. Towards this, in this paper, we outline three different problems in the context of evolving automotive software and discuss potential solutions for each of them. First, we outline a framework that can accommodate variability in specifications while developing software for automotive product lines. Secondly, a technique is illustrated to addresses after-sales addition of new features in existing systems by studying corresponding acceptable performance degradation of existing functionalities. Finally, we discuss how an inconsistency management framework and regression verification can ensure consistent evolution of engineering processes for automotive mechatronic systems.

Automated production systems need to be thoroughly tested. For this, the fully integrated system, consisting of software and hardware, is investigated for remaining bugs using system tests. Unlike unit tests, these tests involve human interaction with the system and are mostly performed manually, without any tool support for their simulation or adequacy examination. Thus, important functionality can be overlooked during testing, decreasing the overall system quality. Coverage measurement during testing can be a valuable support for assessing test adequacy, but requires instrumentation that might influence the real time behavior of the system. Further, test adequacy cannot be calculated until all tests have been executed. To tackle these problems, we propose an approach that aims at estimating coverage criteria prior to system test execution at the aPS and without instrumenting the code or defining hardware simulations. To this end, the nominal hardware behavior and human interaction models are derived from existing integration tests, enabling a priori estimation of the system test coverage via simulation of the software along with the deduced behavior.

Modern manufacturing systems typically require high degrees of flexibility, in terms of ability to customize the production lines to the constantly changing market requests. For this purpose, manufacturing systems are required to be able to cope with changes in the types of products, and in the size of the production batches. As a consequence, the human-machine interfaces (HMIs) are typically very complex, and include a wide range of possible operational modes and commands. This generally implies an unsustainable cognitive workload for the human operators, in addition to a non-negligible training effort. To overcome this issue, in this paper we present a methodology for the design of adaptive human-centred HMIs for industrial machines and robots. The proposed approach relies on three pillars: measurement of user's capabilities, adaptation of the information presented in the HMI, and training of the user. The results expected from the application of the proposed methodology are investigated in terms of increased customization and productivity of manufacturing processes, and wider acceptance of automation technologies. The proposed approach has been devised in the framework of the European project INCLUSIVE.

Today's industry increasingly requires flexibility and adaptability in the development of automation control software, especially for automated production systems (aPS) including an automated material flow system (aMFS). To meet these demands, the development of the automation control software and the modification during the operation of the aMFS has to become as easy as possible without taking a lot of time. Using a proven approach from computer science that describes the separation of the control software in different layers in order to meet high-level and low-level requirements individually, the results of a feasibility study regarding the implementation of this approach for aMFS are shown. Moreover, to deal with the complexity of developing the control software, a model-based development approach is applied. In order to model aMFS, the modeling language has to cover different aspects such as logistics functions, different interfaces and abstractions of control levels compliant to international standards. Furthermore, a feasibility study how to implement such model-based two-layer architectures considering the commissioning of an industrial aMFS is shown.

Shorter innovation cycles along with the demand for individualized, high-quality products pose high challenges on today's automated material flow systems. These systems, suitable for producing bulk goods, but inflexible in case of product changes, need to react flexibly to unplanned configurations. Especially in the software domain retroactive adaptations are tedious work and prone to errors. By deploying object-oriented programming methods in IEC 61131-3, the modularity already existing in the hardware domain of automated material flow systems could be transferred into the software domain. To meet the demands of automated material flow systems, the modular software remains centralized and executable on programmable logic controllers. In this paper a concept is introduced which facilitates the retroactive addition or removal of sensors or actuators in automated material flow systems based on object-oriented programming methods.

During the lifecycle of automated production systems (aPS) additional functionalities and evolutions are realized. As a consequence, control software of aPS becomes highly complex and hard to maintain, which rises the need for evaluating and improving the quality of the control software. Tools for assessing and analyzing the quality of control software are rare and mostly lack accessible platforms that allow the use of analysis data by quality officers and stakeholders. Therefore, this paper presents a cloud platform for code-analysis, a developed tool for evaluating control software by means of Semantic Web technologies. The scalability of the cloud platform supports varied volumes of data and allows efficient use of the analysis data in a continuous integration system for trend analysis in combination with software quality model that can indicate the overall quality level of the control software.

High requirements regarding quality are set for automated production systems (aPS) as malfunctions can harm humans or cause severe financial loss. These malfunctions can be caused by faults in the control software of the aPS or its inability to correctly identify and handle unintended situations and errors in the technical process or hardware behavior. To achieve more dependable control software, software testing and formal verification can be used to find faults in the software, but require to make assumptions about possible situations (inputs) occurring in the aPS during runtime and often only allow the validation of specific cases. Monitoring individual functions within the control software during runtime can help to identify unspecified situations and raise warnings of the uncertainty about the suitability of a reaction. Yet, the design of reliable monitoring functions requires extensive experience and resources. For this reason, we propose a method for generating monitoring functions from available testing and verification specifications initially used for validating a control software function. Through this, it is possible to continuously assess the behavior of individual software functions and to identify and warn about a) violations of the test specification during runtime and b) unintended situations in which correct software behavior was never tested. Thus, the approach can help to assess and improve both the control software and specification quality through observation and behavior assessment far beyond the testing phase by efficiently reusing existing test specifications for runtime monitoring.

Cyber-Physical Systems (CPS) could be the most modern electronic development as yet, thanks to the integration of information and communication technology (ICT). CPS has associated with computer systems (cyber part) which are closely related to the real-world processes (physical part). A CPS is supported by the newest and foreseeable further advances of computer science, data and communication equipment on the one hand, and of manufacturing science and tools, on the other. On the contrary, within the multiple applications, there are CPS for manufacturing systems, called CPPS (Cyber-Physical Production Systems). The fourth industrial revolution regularly distinguished as I4.0 is based on CPPS. Considerable numbers of authors agree that paradigms agent-based as Multi-Agent Systems (MAS) converge or they make up some parts to apply CPPS. In general, this paper emphasizes that there are different important approaches in CPPS implementation which point near, in particular to MAS. The objective of this article is to provide general and specific concepts associated CPPS implementation through agents, considering the current multiples approaches and methods. A key result is that Agent-Oriented Software Engineering (AOSE) methodologies have been a highlight to comparisons leading benefits to apply in CPPS.

A much discussed topic in the recent years is the interconnectedness of industrial plants in the field of Cyber-Physical Production Systems (CPPS). In the future, the data and aggregated information from various production plants will be available globally at any time. Particularly in maintenance, this could be a helpful information expansion for the maintenance staff, since maintenance information are not only available from one plant but also from several, similar plants. However, this information has to be presented to the worker in a suitable way, because especially in maintenance tasks, the worker often needs both hands, and his/her complete visual attention to the task. This paper presents an approach for a speech interaction system that exchanges data and information over a cloud-based system with a manufacturing plant. The worker exclusively interacts with the plant over wireless headphones and a microphone. The communication between the plant, the cloud, and the speech interaction system is described as well as use cases for maintenance and training. The approach was applied in a case study using a demonstrator plant with various use cases.

For Industry 4.0 technology as well as Cyber-Physical Production Systems analysis of data gained more and more importance. But the disparity of data, often make the efficient use of data mining methods difficult due to data with poor quality. To evaluate data quality and further adopt appropriate measures, the proposal develops a data quality model fitted to the specific properties of signal data of industrial processes. Relevant data quality characteristics are identified and a classification of these characteristics is conducted to ascertain important factors. Furthermore, a measurement for the characteristic Completeness, aggregated of its sub-dimensions, is defined. The data quality model is applied to two different use cases showing its effectiveness and validity of the defined measures. The efficient use of real industrial signal data e.g. appropriateness of the data for the specific data mining purpose, is supported by a comprehensive measurement for data quality and the detailed discussion of the influencing factors.

In this investigation data recorded from a flexible rolling process is processed by data driven methods to develop a quality prediction model for manufactured blanks. A concept for an incremental prediction method is proposed, which takes into account the specific character of the discrete manufacturing process. The method is evaluated based on a data set of process and quality data provided by a test rig. By the example of a specific quality parameter the effectiveness of the proposed method is confirmed. A precise quality prediction model is developed, which predicts the quality value with a high accuracy.

This paper presents a virtual training system for machine operation that is addressing the aging workforce. Targeting the needs and capabilities of aging employees is inevitable to address the ongoing demographic change and remain competitive. The proposed system is composed of a virtual representation of a machine and a control panel on a separate tablet computer. The paper describes the didactic approach, the interaction modalities, and how the system can be adapted to the capabilities of its users. A sample scenario describes the application of the concept in a prototype.

Several research and development activities have been, are, and will be conducted towards the development of agile production systems. All of the will had, have, and will have their individual rationales, aims, and reached results. To enable comparability and a broader application of reached results means for representation of the application field of the research activity are required. As most research and development activities validate their results within a demonstration system one possibility for the representation is a detailed classification of the demonstration system towards agility capabilities of the demonstration system. A first idea of such a classification is given in this paper.

Several research agendas worldwide are targeting the development of Industrial Cyber-physical Systems as the next generation of intelligent embedded devices with improved interaction capabilities. These devices, and their potential uses, are though to deliver a radical increase in system sustainability, reconfigurability and flexibility which is perceived to be the root of the so called 4thIndustrial Revolution. However such technical systems, at the envisioned revolutionary scale, do not exist just yet and require a convergent and multidisciplinary research and development efforts. The academia curricula are also, albeit slowly, adjusting to the emerging education requirements. The Summer School on Intelligent Agents in Automation is a joint effort from several researchers in core areas of the 4thIndustrial Revolution landscape to close the gap and promote advanced education in this context. This paper describes the implementation of the 2ndedition of the event as well as the experience and reflections resultant from it.

In the last decades, Networked Control Systems (NCS) has become a key aspect in different application domains of mechatronic technology (e.g. automotive, railway and industrial automation). This NCS consist of several control nodes equipped with sensors and actuators, which fulfill the specified control tasks and are interconnected by bus systems for realizing an efficient I/O communication between the end nodes and the controlling units. For some applications in these industries, where harm for human life is at risk by actions of the technical system, an architecture involving functional safety is mandatory (e.g. for certification). The development of today's mechatronic systems is widely supported by model-based engineering and design. Yet a model approach and modeling notation framework for designing the architecture of NCS in a functional safe manner is still lacking. In this paper, a notation is introduced describing the architecture, the time behavior and the safety requirements of such NCS. A Human-Robot-Collaboration system with safety aspects is proposed to evaluate the notation as a case study.

The discussion of threats and vulnerabilities in Industrial Control Systems has gained popularity during the last decade due to the increase in interest and growing concern to secure these systems. In order to provide an overview of the complete landscape of these threats and vulnerabilities this contribution provides a tiered security analysis of the assets that constitute Industrial Control Systems. The identification of assets is obtained from a generalization of the system's architecture. Additionally, the security analysis is complemented by discussing security countermeasures and solutions that can be used to counteract the vulnerabilities and increase the security of control systems.

The fourth industrial revolution is shaping the development of companies in the automation domain, encompassing several trends such as Cyber-Physical Systems and the Internet of Things. Even though promising new technologies have arisen in the last years, there are still many challenges to overcome, particularly that of generalized acceptance. In this paper, the latest trends related to requirements and adoption of Industry 4.0 technologies are summarized, as well as the associated issues to be solved. Interesting results from a survey that was given to several industrial partners are presented, which illustrate the current adoption status at the software level, and the extent to which the corresponding challenges are being addressed.

With recent trends in manufacturing automation, such as Industry 4.0, control software in automated production systems becomes more and more complex and volatile, complicating and increasing importance of quality assurance. Test tables are a widely used and generally accepted means to intuitively specify test cases for automation software. However, each table only specifies a single software trace, whereas the actual software behavior may cover multiple similar traces not covered by the table. Within this work, we present a generalization concept for test tables allowing for bounded and unbounded repetition of steps, “don't-care” values, as well as calculations with earlier observed values. We provide a verification mechanism for checking conformance of an IEC 61131-3 PLC software with a generalized test table, making use of a state-of-the-art model checker. Our notation is inspired by widely-used paradigms found in spreadsheet applications. By an empirical study with mechanical engineering students, we show that the notation matches user expectations. A real-world example extracted from an industrial automation plant illustrates our approach.

Availability is among the most important characteristics of manufacturing systems since it affects the productivity of the system. Yet, we argue that current approaches are inadequate for thoroughly analyzing availability of such systems.

This work presents a failure recovery approach for foreseen failures of automated production systems to minimize the downtime of a system due to stoppages. In contrast to the common practice of implementing field control software, we suggest the use of operation states with pre- and postconditions. A set of operation states forms an operation state machine, whereby several operation state machines are used in a hierarchical manner in order to control and observe the process. The meta-model of the Systems Modeling Language (SysML) is extended to combine operation state machines with OMAC State Machines. By dividing the failure detection from the process controller the necessary flexibility is given to adapt this approach to different packaging machines.

Cyber-physical systems constitutes a framework to develop intelligent, distributed, resilient, collaborative and cooperative systems, promoting the fusion of computational entities and physical devices. Agent technology plays a crucial role to develop this kind of systems by offering a decentralized, distributed, modular, robust and reconfigurable control structure. This paper describes the implementation of a summer school aiming to enhance the participants' knowledge in the field of multi-agent systems applied to industrial environments, being able to gain the necessary theoretical and practical skills to develop real industrial agent based applications. This is accomplished in an international framework where individual knowledge and experiences are shared in a complementary level.

After changes to a system in factory automation, testers are under high time pressure to ensure that changes did not introduce new faults, so-called regressions. This is done using regression testing by re-performing system test, i.e. functional test cases relating to the complete automated production system (aPS), to identify whether previously tested functionality is still fulfilling the requirements. Up to now, system regression testing is exclusively performed manually, heavily relying on the ability and stress resistance of the tester. This results in fluctuating regression test quality and unnecessarily prolonged test processes due to choosing and performing irrelevant test cases. In this paper, an approach in which the tester is supported in choosing, prioritizing and performing relevant test cases during regression testing by means of gathering and analyzing field data acquired during the original execution of the system tests is proposed. By using a guided system testing approach and instrumenting the control program implemented in the IEC 61131-3, it is possible to record relevant data during testing efficiently, in line with real-time constraints that are prevalent in the factory automation domain. The concepts and an evaluation based on two performed experiments using a laboratory case study are presented. The evaluation regarding the performance of the prioritizing approach as well as its run-time properties show promising results for an application in the factory automation domain.

Process capability indices (PCIs) are widely used in production processes in order to evaluate the product quality. Taking into account the benefits of these quality indices, six requirements that a PCI must achieve within production processes are proposed by the authors. According to the current industry requirements, the existing PCIs have limitations. Hence, a new multivariate process capability index (MPCI) is proposed. The proposed MPCI (which complies with all proposed requirements) is compared to other MPCIs from the literature and is validated in a German luxury automobile manufacturing company.

The avoidance of plant shutdowns is one of the highest priorities for plant operators (plant owners). Shutdowns are forced by abnormal situations, e.g. unexpected equipment faults such as valve or pump faults. Each unexpected fault can lead to hazardous situations within a plant. Pumps are already well analyzed compared to valves and also frequently used in process industry. In this paper a data-driven fault detection system for valves will be introduced. To gain additional knowledge about faults of specific equipment, big data technology is applied, based on a huge number of historical data for different valves. The paper introduces an approach in which data from different competitive companies operating several process plants are filtered, selected and combined with data from equipment manufacturers. The valve diagnosis system uses historical process data obtained across company borders using physical valve models to detect faults by comparing standardized flow coefficient determined by DIN IEC 60534-2-1.

In terms of current industrial manufacturing sites, a major challenge is to deal with growing complexity by enabling intelligence on the shop floor of existing production processes. A possible solution to reach this goal consists in an integration of smart cyber-physical production systems into the automation systems of a production. One promising approach to do so is based on the agent paradigm. By deploying Multi-Agent Systems into the manufacturing components, each production step is able to gain a self-representation and to achieve intelligent behavior of the entire system. One problem though is the formalization of agent based systems and their communication among each other, which is currently rather hard-coded or application-specific. In this research paper, we propose an architectural approach for a Multi-Agent System that is based on OPC UA as modeling interface and as semantic approach for the integration of agent-based systems into existing manufacturing sites. For this purpose, we define a domain ontology for the representation of intelligent software agents and for the mapping of an agent-based communication by making use of the OPC UA meta model. Due to this conceptual approach, the integration of intelligent entities such as agents into grown manufacturing systems can be performed in a structured and well-defined way as well as by using existing interfaces and semantic standards. The according agent representation intends to upgrade all production resources that can be linked through OPC UA with intelligent behavioral skills. We evaluate the proposed concept by means of an Industry 4.0 demonstrator implementing agents for the representation actual manufacturing machinery based on Raspberry Pi devices.

Cyber-physical production systems' main feature is adaptability. They shall adapt quickly to new requirements such as new products or product variants. Nowadays, the bottleneck is the automation system, which requires high manual engineering efforts for every new adaption. For new requirements, an automation software is created by combining pre-defined software components. But this also means that software components need degrees of freedom in form of parameters, such as timing parameters, to be applicable to new requirements. This paper presents a solution to determine parameters automatically for the automation software of cyber-physical production systems. A scenario from discrete manufacturing illustrates the underlying concepts.

Automated production systems (APS) in factory automation possess a high quality standard and complexity that poses high demands on quality assurance. Especially in later phases of system development, functional tests regarding the integrated behavior of software and hardware, so called system tests, are performed. As methods and tools for system testing are lacking or not applicable in this domain, manual testing currently is the prevalent approach for testing factory automation systems. Due to this and scarce resources during testing, this results in varying test quality as well as minimal documentation. Reproducibility and traceability of performed tests is low, prohibiting assessments of test quality and transparency for customers and the engineering company. In the approach presented in this paper, we aim to increase reproducibility, transparency and measurability of system testing in factory automation by including a human operator in a guided, semi-automatic system testing process via a human machine interface. Through this, the flexibility of the human operator is combined with the precision and resource efficiency of automated testing, not requiring costly simulation models or specialized testing personnel. The approach was developed in accordance with industrial requirements and was qualitatively evaluated with a group of expert using a real industrial automation system.

Increasing numbers of variants and versions in the domain of automated Production Systems (aPS) demand appropriate methods to handle this variability. While software product lines (SPLs), often in combination with feature modelling, are already well established in the domain of information systems, up to now SPLs are rarely used in the domain of aPS. Also, SPLs for aPS need to include variants and versions in mechanic and electric besides software leading to interdisciplinary product lines (IPLs). In this paper an existing approach of an IPL is enhanced with extended feature modelling methods and SPLs and, subsequently, applied to two industrial case studies - one in special purpose machinery and the other in plant manufacturing. Challenges for such automated Production Systems are discussed.

Both information and automated production systems (aPS) evolve during their lifetime, e.g. due to changes in requirements and infrastructure. In order to estimate maintenance effort in information systems the KAMP method is applied. This paper discusses the necessary classification of changes as a prerequisite to apply such a method. Aggravating aPS consist not only of software but also include mechanics and electric/automation hardware. Therefore, the classification has to be enlarged to a multi-disciplinary one. The limitations of this approach for aPS are discussed in detail and demonstrated using three scenarios of a lab size pick and place unit. The paper closes delivering first ideas to cope with these.

To improve the flexibility and robustness of the engineering of automated production systems (aPS) in the case of extending, reducing, or modifying parts, several approaches propose an encapsulation and clustering of related functions, e.g., from electrical, mechanical, or software engineering, based on a modular architecture. Considering the development of these modules, there are different stages, e.g., module planning or functional engineering, that have to be completed. A reference model that addresses the different stages for the engineering of aPS is proposed by the automation markup language (AML). Due to these different stages and the integration of several engineering disciplines, e.g., mechanical, electrical/electronic, or software engineering, information not limited to one discipline are stored redundantly, increasing the effort to transfer information and the risk of inconsistency. Although data formats for the storage and exchange of plant engineering information exist, e.g., AML, fixed domain specific structures and relations of the information, e.g., for automated material flow systems (aMFSs), are missing. This paper presents the integration of a metamodel into the development of modules for aMFS to improve the transferability and consistency of information between the different engineering stages and the increasing level of detail from coarse-grained plant planning to fine-grained functional engineering.

The papers in this special section focus on the concept of Industry 4.0. From its origin, Industry 4.0–derived from the German term Industrie 4.0–is used as a synonym for Cyber-Physical Production Systems (CPPS), i.e., Cyber-Physical Systems applied in the domain of manufacturing/production. To enable CPPS their automation systems need to be enabled to fulfill the requirements. The term Industrie 4.0 was first used in 2011 at the Hannover Fair and the topic has grown every year not only on the fair. There are still several definitions of Industrie 4.0 (I4.0).

In today's automated systems, the plant operator is confronted with a growing amount of diverse and distributed data about the plant process. For process control, the operator has to observe, interpret, and integrate the process data to form a basis of decision making for input parameter settings. This difficult task is prone to errors and can quickly result in insufficient product quality. An effective display design can support the operator and mitigate this effect. Two experiments investigated whether the integration of process data in 3-D visualizations could increase the operators’ performance in this environment. The first experiment examined benefits of improving reaction times and error rates for problem detection and corrective inputs. The possible reduction of the operators’ workload was examined simultaneously. Additionally, experiment 1 offered insights on how interaction with the 3-D visualization could further improve the appropriateness of selected process settings by the operator. Results of this experiment showed 3-D and interaction as beneficial factors for the detection of problems in process control tasks and participants showed a low mental workload compared to 2-D presentations. In the second experiment, the scenario was extended by the investigation of a 3-D input design. In comparison to regular 2-D input, results showed that a combination of 3-D input and interaction exhibited higher accuracy in problem solving.

In the development of PLC software for automated Production Systems the method copy & paste is commonly used. However, this method leads to various disadvantages. To avoid these disadvantages, a parameter-based concept for planed reuse is presented in this paper. By means of the Siemens PLC software of an automated warehouse, the concept is defined and evaluated. In a first step, the PLC software is analyzed and reusable parts as well as variation points are identified. Subsequently, it is demonstrated that the warehouse variants can be clearly described by a suitable set of parameters. In a last step the automatic configuration of the PLC software by using the identified parameters as well as a Step7 source code template is explained and evaluated.

In recent years automation systems have been required to provide characteristics of flexibility and efficiency in order to cope with the fast changes of the market and consumer demands. To deal with these requirements, the industry has started to implement reconfiguration techniques in order to allow the automation system to reschedule the production, recover from faulty situations and to update the controller at run time, among others. Previous works of authors have been aimed at a model-based definition of the plant components and operations, related to a product. Additionally, automatic code generation has been proposed to obtain an optimal control sequence. This paper extends these previous works with an architecture that generates and updates the software architecture of a Programmable Logic Controller during runtime, in response to changes on the plant layout, the product to be manufactured or the control hardware.

The demand for mass-customized products is increasing steadily. As a consequence, the requirements regarding flexibility and efficiency of automated production systems and their logistic parts are increasing as well. In order to evaluate different plant designs before the commissioning or due to necessary reconfigurations, model-based approaches in combination with simulation techniques can be applied. Therefore, the interaction of transported load and physical aspects of plant components, e.g. motor current, needs to be described and evaluated. To bridge the gap between the time-discrete behavior of manufactured products, i.e. their occurrence, and the continuous characteristics of a transportation system's modules, i.e. differential equations, in this work a model interface was developed and integrated into model classes of the Modelica modeling language. The correct behavior of the particular library items has been proven by tests of the logical behavior and the comparison to a real conveyor belt drive system.

Automated production systems are usually driven by Programmable Logic Controllers (PLCs). These systems are long-living and have high requirements for software quality to avoid downtimes, damaged product and harm to personnel. While commissioning multiple systems of similar type, pragmatic adjustments of the software are often necessary, which results in two or more similar variants of initially identical software. For further evolution of the software, an equivalence analysis of the software's behavior is beneficial to merge divergent development branches into a single program version. This paper presents a novel method for regression verification of PLC code, which allows one to prove that two variants of a plant's software behave identically in specified situations, despite being implemented differently. For this, a regression verification method for PLC code was designed, implemented and evaluated. The notion of program equivalence for reactive PLC code is clarified and defined. Core elements of the method are the translation of PLC code into the SMV input language for model checkers, the adaptation of the coupling invariants concept to reactive systems, and the implementation of a toolchain using a model checker. The approach was successfully evaluated using the Pick-and-Place Unit benchmark case study.

Industrie 4.0 requires intelligent self-aware adaptive machines and plants that know their abilities. In this paper, we propose different qualitative and quantitative measures to evaluate reconfiguration abilities of automated production systems in the context of Industrie 4.0. This paper presents first measures to quantify different reconfiguration abilities of automated production systems, using different adaptive strategies focusing on fault compensation. For an application example, a lab size plant, the measures will be explained and calculated.

Industrial manufacturing processes are complex processes, where transparency of every process step is necessary to achieve a high level of quality and efficiency. In order to achieve this transparency, manufacturing execution systems (MES) are used. However, as these systems are very expensive, mainly due to individual programming effort, MES usage is oftentimes limited to larger companies. To ultimately reduce implementation costs for MES, the current research project AutoMES proposes a standardized, model-based approach to facilitate automatic generation of MES functions. This paper presents requirements on a suitable modeling language, as well as how these requirements are fulfilled by the modeling language used in the AutoMES project. The modeling language is an extension of the MES Modeling Language (MES-ML), a modeling language for the specification of MES. With the use of the extended MES-ML it is possible to generate a generic, machine-usable MES specification, suitable for code generation. To evaluate the proposed modeling language extensions, an industrial brewing process has been modeled and verified by MES engineers during the project AutoMES.

The specific challenges for maintenance of software and its related hardware for the domain of automated Production Systems is discussed. Presenting four industrial case studies from renowned and world market leading German machine and plant manufacturing companies, these challenges and different solution approaches are introduced with a focus on software architectures to support modularity as a basis for maintaining long-living automated Production Systems. Additionally, most critical aspects hindering classical approaches from software engineering to be successful, e.g., modes of operation and fault handling, are discussed. In the last decades, research in the field of software engineering for automated Production Systems (aPS) has been focusing on developing domain specific model-driven engineering approaches supporting the development process, but mostly neglecting the operation, maintenance and re-engineering aspects. However, the success of model-driven engineering in aPS industry has been limited because the effort to introduce model-driven engineering and to change the entire existing legacy software is estimated as too high and the benefit as too low against the background of customer specific solutions expecting a low degree of reuse.

In order to be prepared for future challenges facing industrial production domain, CyberPhysical Production Systems (CPPS) consisting of intelligent entities which collaborate and exchange information globally are being proclaimed as the 4th industrial revolution. In this context, the service-oriented architecture as well as agents are often proposed. Agent technologies have been studied intensively in academia within the last decades and applications are recently available successfully to the production domain. The applicability of agent technology and smart data for realizing CPPS with real time and dependability requirements is discussed and shown based on selected scenarios. The talk concludes with opportunities and open research issues that need to be faced in order to achieve CPPSs in the context of Industry 4.0.

For manufacturing industry seamless workflows along the whole product lifecycle are crucial for sustainable success. Although the integration of all used systems to a single supersystem is not reasonable, the integration of data is essential. This contribution introduces a model-based concept to integrate data elements of distributed data systems and sources to one virtual database. Specific view models represent the information about data, their relations, properties and environments. The combination of these pieces of information allows the configuration of a middleware platform to establish the interconnection of related data elements across distributed IT systems. This approach tries to feature the missing link between the successful enterprise application integration concept, sophisticated XML-based technologies and established graphical description languages. Instead of a fixed development environment this approach introduces how to adapt and utilize UML and BPMN for a modular and parallelizable integration process to archive a highly company-specific but user-oriented data integration environment.

The increasing demand for adaptive and flexible machines and plants raises the need for components and systems that can be dynamically reconfigured. Therefore, production facilities are especially needed to allow for connecting or disconnecting machine modules at runtime to allow for flexible production orders and varying product types. Many highly sophisticated approaches e.g. multi-agent systems and service-oriented architectures support reconfiguration at the application layer, but the communication link between the components' hardware has not yet been appropriately considered. However, the deterministic behavior of a field-bus is required to perform such a system reconfiguration of plants without violating the maximum cycle time of control applications. Considering these constraints, two different approaches are presented and discussed in this paper - a single-master and a multi-master approach. Based on an extension of the standard EtherCAT state machine, it is shown how flexibility can increase by reconfiguration of the field-bus communication. An evaluation of the reconfiguration time illustrates the impact to the control application.

Due to the rising installed power of distributed regenerative energy sources and enhanced flexibility by the installation of local house batteries and battery electric vehicles, todays power grids are facing new challenges. In rural areas farmers are using their roof surfaces to harvest sun energy with photovoltaic cells. This leads to the fact, that small groups of houses may produce more power than the transformer is able to feed into the grid. This paper examines a supervisory control method to route the locally generated energy intelligently to demanding consumers and to incorporate local power storages. We propose a design approach that uses the viable system model by Stafford Beer for deriving a hierarchical structured but also distributed control system. The layers of the model possess agents that bargain between local components and beyond household border to act as energy management. Hence it results in a market-based behavior to route the energy optimally. Eventually this cybernetic agent architecture is implemented as MATLAB/Simulink model and evaluated in the scenario of a rural grid.

Machine and plant automation (M&PA) is becoming more and more complex, especially, as additional functionality is realized by a rising amount of software. In order to meet high requirements regarding the quality of these machines and plants, sophisticated methods for quality assurance (QA) are needed. Interaction diagrams have been established throughout different domains for specifying use cases, requirements and test cases and therefore are an essential part of quality assurance. In this paper two approaches aiming at supporting QA in machine and plant automation, namely Message Sequence Charts (MSCs) in Model-based Integrated Requirements Analysis (MIRA) and Unified Modeling Language Sequence Diagrams (UML SDs) for Programmable Logic Controller testing (UMLSD4PLC), are compared and analyzed regarding their benefit to QA.

Modeling complex mechatronic systems containing parts such as mechanics, electronics and software is a major challenge for the engineering of automation production systems. As a result, many modeling languages as well as tools (e.g. petri nets or MATLAB/Simulink) and verification approaches (e.g. testing, simulation or formal verification) that focus on special challenges to model the mechatronic system correctly have already been developed. However, a holistic approach combining the strengths of the individual approaches adapted for the engineering of automation production systems has not yet been developed. In this paper we consider the combination of simulation with MATLAB/Simulink and a discrete event simulation model allowing model checking of specific constraints. Hence, the strengths of both approaches' formal verification, i.e. to identify not only faults but also correctness of a system, and simulation, i.e. showing the exact time behavior of the system, can be combined. A key challenge of our approach is to identify an appropriate level of abstraction of both models. The evaluation of the presented approach is introduced by modeling the throughput of a simple automation production system.

Automated machines and plants are operated for some decades and undergo an everlasting evolution during this time. In this paper, we present three related open evolution challenges focusing on software evolution in the domain of automated production systems, i.e. evolution and co-evolution of (interdisciplinary) engineering models and code, quality assurance as well as variant and version management during evolution.

Automated production systems involve multiple engineering disciplines and often operate for several decades. Therefore, in order to leverage benefits of model-based system engineering, modeling approaches must handle both multiple disciplines and variability. As a first step towards variability modeling and management, a small case study is carried out to investigate the opportunities and challenges when enhancing an interdisciplinary modeling approach with delta modeling - a technique for specifying variants and versions. The paper reflects critically its applicability, implications as well as potential beneficial impacts and discusses encouraging challenges and next steps towards an interdisciplinary approach for variability modeling and management.

Information and automated production systems are long-living, evolvable systems. Consequently, modifications are performed to correct, improve or adapt the respective system. We introduce different approaches to analyze maintainability of software-intensive systems and propose two different case studies from the information and from the automated production systems domain as a basis to validate approaches on system evolution.

Automated production systems (aPS), a specific class of mechatronic systems, are complex, long living, software intensive, evolving systems designed according to customer request. Often, the evolution of such systems has to cope with imponderables and sudden disturbances of the systematic development or maintenance process. In software engineering, the concept of technical debt and, in more detail, architectural technical debt has been introduced recently to describe phenomena, which increase software development costs over time. This paper tries to adapt and apply the classification of technical debt and architectural technical debt to automated production systems, identifying similar dimensions but adding specific challenges, causes and their effects. Once the causes and effects are identified, management and recovery strategies coping with technical debt and architectural technical debt shall be developed in the future, thereby enlarging the strategies from software engineering. The adapted classification and adaptations are based on the experience of several industrial projects in aPS.

Coping with evolution in automated production systems implies a cross-disciplinary challenge along the system's life-cycle for variant-rich systems of high complexity. The authors from computer science and automation provide an interdisciplinary survey on challenges and state of the art in evolution of automated production systems. Selected challenges are illustrated on the case of a simple pick and place unit. In the first part of the paper, we discuss the development process of automated production systems as well as the different type of evolutions during the system's life-cycle on the case of a pick and place unit. In the second part, we survey the challenges associated with evolution in the different development phases and a couple of cross-cutting areas and review existing approaches addressing the challenges. We close with summarizing future research directions to address the challenges of evolution in automated production systems.

This paper presents the modular automation for reuse in manufacturing systems (modAT4rMS) approach to support the model-driven engineering (MDE) of object-oriented manufacturing automation software with regard to its usability and software modularity. With usability, we refer to the aspects' effectiveness, efficiency, and user acceptance as defined by ISO 9241-11. The modAT4rMS notations are based on selected features from the unified modeling language (UML) and the systems modeling language (SysML), and iteratively further developed by a series of empirical studies with industrial practitioners, as well as mechatronics trainees. With modAT4rMS, an MDE approach for programmable logic controller (PLC) programming was developed with the goal to facilitate modular object-oriented programming of PLC software by improving the representation of the relationships between the structure and behavior diagram types, and by reducing the level of abstraction in the structure model. modAT4rMS notations for PLC software structure and software behavior modeling are presented and illustrated with a modeling example using a modAT4rMS editor prototype. For the evaluation of the developed notations, the results from a study with 168 participants are presented, showing the benefits of this new approach in comparison with the classic procedural paradigm (IEC 61131-3) and the domain-specific UML profile (plcML) in regard to programming performance and usability aspects. Finally, the advantages and limitations of the approach are discussed, and an outlook for further development is given.

Based on previous studies on programming errors and their causes, the presented study examines UML modeling failures in the area of teaching machine and plant automation that were made by 102 mechanical engineering students and analyzes their reasons. Though poor concentration, misunderstandings and overlooking of key aspects within the task have been mentioned by about one third to one fifth of the subjects, they could not explain the objective modeling performance. Instead, the main reason for the shortcomings in the subjects' UML models seems to be difficulties to “translate” their mental model of the system into an UML model. In particular, structure modeling was quite difficult to handle. Lack of time also was a problem, but resulted primarily from starting with the rather complicated structure modeling part instead of the behavior modeling part.

Teaching large classes with about 1500 freshmen of mechanical engineering in modeling automata, model driven approaches and C-programming is a challenge for every university lecturer. Since effective learning requires feedback and interaction, e.g. approval of program correctness, hints for improvement etc., a web-based system called PIT (Prüfungssystem Informationstechnik, engl. examination tool of information technology) has been developed and introduced in 2010. Students homework and tests during the semester are done interactively with PIT, giving tutors the time to assist students and not only to review their homework and tests. Hence, there are two substantial benefits: A more effective education and support for the students and a reduction of the costs for the institute. Over the past few years, PIT has been extended regularly. In 2012 the addition of a new plant programming exam was done with great effort. In the summer semester of 2013 the new exam was obligatory for the students for the first time. Altogether, every student had to do three e-learning exams, one plant programming exam and finally one written exam. A detailed evaluation of the exams showed a great improvement of performance by female compared to male students and the benefits of the new plant programming exam which are presented in this paper.

Automation Systems have to deal with breakdowns of their components which potentially lead to drop-off in production and high follow-up costs. The current solution for this problem is the use of redundant hardware ensuring high availability but causing high costs. Starting from nowadays often used distributed automation systems, this paper proposes a software-based solution for high availability using automatic redeployment of control software during runtime avoiding high costs for redundant hardware. Existing approaches are enhanced concerning real-time and distributed I/O. The proposed solution replaces manual deployment and increases flexibility of automation systems.

Software engineers of production automation systems have to deal with a great number of various tasks for example programming the automation code, simulation or visualization of the production system. For the virtual or real commissioning of the system, further tasks like defining software or hardware communication interfaces are needed. Most engineering tools used by the software engineers only support one or few of the tasks. As a consequence, there is a great heterogeneous industrial tool landscape, which has to be applied in automation software engineering. Although every tool is specialized in its domain, there is plenty information about the system, for example parameters describing geometries or sensor/actuator signals, that are used by almost every domain. Currently, tool independent parameters are declared and defined in every tool redundantly. The possibility to declare and define parameters globally only once is missing. This fact increases the risk of inconsistency and the effort in software engineering. In this paper the approach of a consistent engineering information model to improve the consistency of information focused on parameters is introduced. The approach is implemented by Automation Markup Language (AutomationML). The evaluation is realized by the software engineering of a pneumatic double acting cylinder and shows the potential of accelerating the software engineering and improving consistency of parameters through introducing a consistent engineering information model.

Due to market requests many production systems undergo an everlasting evolution process that increasingly shifts traditional development activities for production systems to later phases of their lifecycle. As one of these activities, this contribution aims on the need for a semi-automated requirement verification mechanism during evolution. In this context the contribution proposes an answer to the question how a posteriori as well as a priori verification can be combined and which synergies arise accordingly. To do so, two appropriated approaches, one using an interdisciplinary modeling framework for model-driven engineering, and one using a PLC-signal based monitoring technique are presented that enhance each other by comparing estimated and actual system characteristics as verifiable requirement description. The resulting combined approach and its synergies are illustrated in two scenarios of an evolution case study.

In production automation, field level control software is executed on programmable logic controllers and implemented according to the IEC 61131-3 standard. In contrast to sophisticated architectural capabilities in business software and high level programming languages, the IEC 61131 standard provides only limited support for architectural designs. Nevertheless, architectural decisions are made during implementation. Within this paper, a novel approach to discover implemented architectural designs within existing field level automation software is proposed and a framework for analyzing field level automation software, following a sophisticated viewpoint-based software engineering paradigm, is presented.

Industrial plants are multi-disciplinary systems that are operated for multiple decades. Changes in these systems are consequently indispensable, making appropriate mechanisms for managing co-evolution of engineering documentation necessary. In this paper, a co-evolution model for control and simulation software is introduced. Typical evolution categories and modification strategies for enabling co-evolution of automation control and simulation software are derived and formally defined. Using description logics, the identification of these complex modification strategies based on atomic modification actions is made possible.

To manage the increasing complexity of automation software, new development methods are needed. The model-driven development (MDD) approaches are very promising here. However, switching to the MDD in industrial software development practice is complicated, and required efforts are often very high. Therefore, the MDD approaches are still far from the widespread industrial usage. With the presented approach the next steps towards the industrial application of MDD technologies are performed. The approach is not intended to replace existing development practices in industry, but it aims at the step-by-step introduction of MDD technologies into industrial software development.

Changes of software in factory automation are frequent and resource-consuming in quality assurance when proving compliance with functional specifications. Automated testing can help minimizing required resources for software engineering. However, changes in the software cause a need for re-evaluating functionality through tests. To reduce resource consumption, existing relevant tests can be re-executed after ensuring their compatibility with the software after the changes. In this paper, an approach for enabling the automatic identification and classification of changes is presented, which can be used to efficiently select existing and help adapting incompatible test cases, leading to an overall increase in test efficiency. The approach is based on a detailed Programmable Logic Controller (PLC) program library and code analysis (both in IEC 61131-3) for several types of machines of industrial companies from the domain of factory automation.

Due to increasing functionality and use of software within mechatronic systems, their complexity increases. Quality and correctness of these systems' functionality must be ensured making an approach for requirements and test case management necessary. To provide a first step towards solving these challenges, this paper presents a modeling approach that enables the early integration between requirements and test cases. Moreover, a first study on using semantic technologies for ensuring consistency is presented. By that, the foundations for supporting test engineers in specifying and testing the system are formed.

In the paper, a SysML-based approach that supports the model-driven engineering (MDE) of manufacturing automation software is presented. The approach adapts the Systems Modeling Language (SysML) and defines a specialized language profile, the SysML-AT, to support automated code generation for IEC 61131-3 runtime environments. For adapted SysML Parametric Diagrams (PD), a prototypical tool support was realized inside a commonly used automation software development environment. Inside the prototypical tool, the linking between the realized PD model editor and online data from the IEC 61131-3 runtime environment enables direct debugging within a SysML-AT model. The concept was evaluated by case studies and by usability experiments, which proved the usability of the SysML-based MDE approach for future users.

Due to fierce competition, short product lifecycles and fluctuating markets, there is an increasing demand for flexible and fault tolerant systems in automated manufacturing. Multi-Agent System (MAS) can help meeting these demands by enabling fast reconfigurations and smart fault handling and compensation. A previously developed MAS, distributed on real time and non-real time capable platforms, can increase these benefits by combining the advantages of both, real time and non-real time implementations of MAS when involved in controlling (a part of) a manufacturing plant. Even though different communication concepts have been developed and are provided within this MAS architecture, connections to other industrial IT systems such as Enterprise Resource Planning (ERP) or third party MAS implementations have not been included. To enable maximizing the system's flexibility, a complete immersion of the MAS into the industrial IT environment needs to be provided. This paper proposes communication concepts, a running example and evaluation of the concepts for this problem.

Todays increasing volatility of market demands and customer requirements are forcing industrial enterprises to realize and ensure an increased flexibility of production systems. Since current automation concepts and architectures for production systems do not provide the required flexibility sufficiently, new approaches have to be developed. This paper proposes an approach that implements the quickly evolving concept of Cyber-Physical Systems for the special case of production systems by means of software agents. A joined demonstrator of such Cyber-Physical Production System is described and used for the evaluation of the proposed multi-agent approach.

This paper presents the gained experience from the introduction of an Web-based e-learning platform and the affiliated teaching techniques in a very large class (1400 students) at the Technische Universitát München (TUM). The students are 1st semester bachelor students of mechanical engineering. The focus of the introduction was to give students the opportunity to exercise programming and modeling languages online and to evaluate their usage for future findings about the corresponding learning mechanisms. This paper focusses on the advantages for the lecturer in different phases of the course and gives an overview on the correlation of e-learning and other aspects of the course.

The results of the evaluation of a web 2.0 platform promoting interactive learning for technical vocational training are presented. The focus of the paper lies on an analysis of the user interests in that particular context. It was found that despite the industrial-technical trainees' high affinity with computers and multimedia, they prefer a rather passive (traditional) use of the internet and its elements.

The field of system integration features various approaches from the standardization of data structure to the development of universal exchange formats, but lacks a visualization as well as a superordinated structuring concept. Within the IMIP project a model-based system interconnection was developed that combines a star-shaped coupling architecture with a task-specific model framework. All systems of an enterprise are connected to the central data backbone via a modular interface. There the actual data exchange is carried out by the interplay of mapping and transformation engines. The information needed by them is divided task-specifically into eight model types. Together with the UML based meta-model oriented to interconnection tasks this approach shall improve usability and applicability and provide a stable basis for the integration of further research.

Integrating Multicore Programmable Logic Controllers (PLC) in Networked Automation Systems (NAS) promises a higher computing performance and PLC manufactures are launching their first Multicore PLCs (MPLC). However, analyzing the MPLCs time behavior considering distributing automation tasks on different cores is more complex than analyzing the time behavior of single-core PLCs. For increasing analyzability of NAS a modeling notation is needed. However, MPLCs are not considered sufficiently in modeling NAS nowadays. In this paper, properties and requirements of MPLCs regarding the distribution of automation tasks on multiple cores are explored. These properties and requirements affect real-time constraints of automation tasks of a Multicore-Capable NAS (MCNAS). Based on the explored properties and requirements a modeling notation for modeling MPLCs is proposed. This modeling notation offers the automation engineer higher analyzability of the time behavior of MCNAS.

Industrial plants are operated a long period of time. Hence, evolution of these plants is necessary to cope with technological developments or market requests. However, evolution cycles of disciplines involved in cooperative design of such plants are different. Therefore, further research needs to be investigated in order to provide an integrated variant and version management, i.e. evolution management, of such plants. As a first step towards solving this issue, this paper presents an abstract model for industrial plant evolution and analyses it using a detailed case study in the industrial plant automation domain.

Increasing system complexity and flexibility are two major trends in production systems leading to increasing problematic control architectures and implementation processes. Often discussed solutions for this problem are agent oriented control systems and agent technology enabling a more suitable management of design, implementation, and use complexity. Within this paper best practice reached in this field in the last years are summarized. They will give a short but consistent overview about the current borderline of the applicability of agent oriented control systems in production systems. Within this paper.

In the field of automotive engineering the development time is shrinking while the variety and complexity of vehicles is increasing steadily. Since the measuring process for evaluation of the component behavior is time-consuming, model based development is aspired. This paper describes different modeling techniques and presents an architecture to build up vehicular mechatronic systems considering the complexity and product variety. It presents how these methods can be applied to create a vehicular part: variable valve timing systems. According to the proposed architecture different layers of the real component are indicated and represented by the particular model. Subsequently the simulation results of the Valvetronic are evaluated by means of the measurement data. Concluding, the prerequisites for the acceptance of model based engineering are described.

This paper presents the elaboration of a concept to develop and implement real-time capable industrial automation software that increases the dependability of production automation systems by means of soft sensors. An application example with continuous behavior as it is a typical character treat of process automation is used to illustrate the initial requirements. Accordingly, the modeling concept is presented which supports application development and which is supplemented by an implementation approach for standard automation devices, e.g., programmable logic controllers. The paper further comprises an evaluation which adapts the concept for two use cases with discrete behavior (typical character treat of manufacturing automation) and validates the initially imposed requirements.

In this paper, the structure and the simulation results of a model between Simulink and different PLC environments (integrated development environment) will be compared. The transformation of the simulation model from Simulink into a PLC code, which is compatible to the investigated PLC environments, is realized with a self-developed code generator. Continuous Function Charts (CFC) is used as target language for the PLC environments. The investigated PLC environments are TwinCAT (Beckhoff), PCS7 (Siemens) and PC Worx (Phoenix Contact). For the comparison, a simple speed control model was designed in Simulink. The code transformation and evaluation of the structure and simulation results show, that the PLC environment of Beckhoff has the best accordance in structure with the simulation model in Simulink. However, the PLC environment of Siemens shows the best accordance the simulation results with Simulink.

The consequence of the increase of automation and therefore the growing complexity of automation tasks is a necessity for using distributed control architectures. This implicates a support for developers in the engineering of such distributed automation systems. For this reason, this paper focuses on the design support of distributed automation systems by use of design patterns. Important aspects are the consideration of non-functional requirements in design patterns and the integration of design patterns into the engineering workflow. This paper presents a design pattern template which supports developers with predefined automation functions which are assigned to a system function and in their selection of an appropriate distribution of automation functions, taking relevant non-functional requirements into account.

In automation, the suited scheduled maintenance is one of the keys for plants' operation in order to minimize plants' shutdown. Usually, maintenance is time-interval based or operation-time based to prevent plant shutdowns by means of displacing aged automation devices early. This causes an increasing expenditure due to the fact that the replaced automation devices could be in operation longer and the plant has to be stopped for replacing the device. In this paper we present an approach focusing on combining engineering data and historical process data to extract additional information and applying analysis methods for diagnosis. We introduce how to find cause-effect dependencies of failures during abnormal plant situations to forecast abnormal and critical plant situations.

When designing a distributed control system consisting of heterogeneous components whose properties are usually not well known, it is difficult or even impossible to use an analytical approach to verify the fulfillment of all requirements. In this contribution, it is shown (at the example of different implementation scenarios for a typical control function) that detailed knowledge about the communication system is required to reliably assess and compare different designs of distributed systems with regard to a real-time characteristic. However, automation engineers typically can neither be expected to have such detailed knowledge about the internal details of computing and communication, nor do they have the time to study different design alternatives in detail. This motivates further work of the authors towards better engineering support for the design of distributed automation systems, by providing helpful advice regarding design decisions.

Current production plants are highly customizable and flexible in their processes. This flexibility is mainly realized by software. Standards like IEC 61508 for functional safety demand intensive testing efforts to ensure a certain level of integrity. The uncertainty about system behavior, especially in fault situations, complicates reliability testing. We propose a concept that integrates fault models into SysML system models for model based development. The introduced fault models can be designed in two granularity levels, starting with coarse information at early development phases up to precise mathematical relations. The concept is applied to a component (rotary switch) of a laboratory plant, which connects three conveyor bands where friction deviation can occur during rotary movement. Such models can build the foundation for test generation in reliability testing.

Multi-agent systems (MAS) are used to increase flexibility and reliability of automated production plants, by reducing their control structure's rigidness through the use of autonomous, interacting software agents. This paper comprises a concept for MAS, which are implemented partly on the Manufacturing Execution Systems (MES)-layer and partly on the field device layer, trying to combine the advantages found in other works that used MAS on only one of these layers. The concept contains a software architecture, a communication structure between different runtime platforms (MES and field devices) and processes, including an adaptive path finding algorithm. An evaluation showed that the proposed MAS is comparable in production performance, while increasing fault tolerance compared to a static legacy implementation.

The Manufacturing Execution Systems Modeling Language (MES-ML) is a graphical modeling language, designed for the requirements engineering and specification of Manufacturing Execution Systems, developed during the project “SpeziMES” funded by the BMBF. The project aimed at the creation of an easily comprehensible and usable language to overcome the interdisciplinary challenges during the specification process of MES. As a method of evaluation, a series of interdisciplinary workshops were conducted to verify the real-world applicability of the MES-ML. This paper presents the project's final evaluation steps, after the workshops had taken place. The language is evaluated by checking whether objective design principles for visual notations are adhered to and by conducting a series of guided interviews with industrial experts.

In this paper an approach to support the development process of closed-loop control software for Programmable Logic Controllers (PLC) is shown. To enhance the development process, a fast and reliable data transfer between different tools and models used during different design phases is realized by model transformation. A core model created using the Systems Modeling Language (SysML) and a corresponding model editor based on the Unified Modeling Language (UML) in combination with a newly realized model transformation from the SysML parametric diagram to the Simulink block diagram is used to integrate the information from different engineering tools and models involved during automation engineering design phases. Hence, a consistent process for the development of closed-loop control software is realized.

Distribution of automation functions onto several controllers has become a widely applied answer to the demands of huge and complex automated systems. Distributed networked automation systems offer advantages regarding various nonfunctional requirements, such as flexibility, availability and maintainability. On the other hand, they impose additional challenges, e.g. for fulfilling the real-time requirements. Control engineers face difficulties in coping with all non-functional requirements when designing distributed networked automation systems, as there are trade-offs between them. A complete and detailed model of the automation system, including all computation and network effects, is usually not available. Therefore, control engineers have a demand for support when making design decisions for distributed networked automation systems. In this contribution, the authors propose an engineering workflow which allows dealing with the design decisions and the non-functional requirements in an appropriate order, and underpins the need for additional support regarding design alternatives.

In machine and plant automation model-driven programming of Programmable Logic Controller (PLC) software is becoming an alternative to the state-of-the-art programming. As part of the related work the authors first present the results of a previously finished project on model-driven approaches for Multi Agent System (MAS) PLC programming, in particular using the Systems Modeling Language (SysML). A tool supported procedure model was developed that assists automation engineers implementing MAS in order to enhance reliability of the plants as well as reusability of implemented modules. Subsequently, related work on the evaluation of PLC programming approaches is discussed and the qualitative evaluation study for our newly developed procedure model is presented. In the last section, we present the study results and discuss our findings and their meaning for future developments and experiments.

Manufacturing Execution Systems (MES) optimize production and business processes at the same time. However, the engineering and specification of MES is a challenging, interdisciplinary process. Especially IT and production experts with different views and background have to cooperate. For successful and efficient MES software projects, misunderstandings in the specification process have to be avoided. Therefore, textual specifications need to be complemented by unambiguous graphical models, reducing the complexity by integrating interdisciplinary views and domain specific terms based on different background knowledge. Today's modeling notations focus on the detailed modeling of a certain domain specific problem area. They do not support interdisciplinary discussion adequately. To bridge this gap a novel MES Modeling Language (MES-ML) integrating all necessary views important for MES and pointing out their interdependencies has been developed. Due to its formal basis, comparable and consistent MES-models can be created for specification, standardization, testing, and documentation of MES software. In this paper, the authors present the formal basis of the modeling language and its core notation. The application of MES-ML is demonstrated taking a yogurt production as an example. Finally, the authors give some evaluation results that underline the effectiveness and efficiency of this new modeling approach with reference to four applications in industrial MES-projects in the domain of discrete and hybrid manufacturing.

This paper presents a UML-based engineering environment for the design and implementation of hybrid systems in forming technology. The discrete superordinate steps of forming technology are programmed as an activity diagram (UML). Some process steps include control loops which are modeled in Simulink. The control loops are automatically generated into Continuous Function Chart (CFC) in Programmable Logic Controller (PLC). The paper describes how the controllers can be distributed with different task cycle times and how the performance of these controllers can be ensured during runtime. In the process, the activity diagram is compared with the standard IEC 61499 considering the aspects usability and execution/implementation.

The reutilization of proven automation solutions for automation systems is an essential approach to increase the profitability of engineering services. Additionally, distributed systems are increasingly important in automation systems. For this reason, this paper focuses on the aspect of distributed systems combined with the reutilization of automation tasks and solutions in the domains of process and production automation. To address this purpose the research project “Functional application design of Distributed Automation systems” (FAVA) was launched. This paper presents the objective of this research project, identifies deficits of the state-of-the-art and describes automation systems which have been analyzed in the FAVA project to identify automation tasks and their variants regarding to the distribution.

Industrial production plants are highly complex mechatronic systems. In today's automation systems a trend for distribution of control functions can be observed. The hereby emerging challenges lead to delays and interruptions in automation projects. Especially non-functional requirements are hard to specify for later engineering phases. Therefore, a holistic engineering approach will be developed. As a first step, important challenges for the development of distributed automation systems are identified in this paper. Based on this, non-functional requirements derived from an information science point of view are adapted to the specifics of automation technology.

An increasing number of functionality in today's machine and plant automation is being shifted from pure mechanics or electronics into software. Therefore the system behavior must be checked for compliance with the demanded software functionality by software tests. The precondition for test case execution is a start state, the system has to take in. In order to evaluate the testability of a system, it must be verified whether the test case triggering system state is reachable. We propose a transformation approach of the control software model and the corresponding environment model (physics) to timed automata for model checking to exploit the power of the query language Timed Computation Tree Logic for start state reachability analysis. The applicability is demonstrated on a laboratory plant.

The growing demand for improved reactivity to market trends faces manufacturing systems with new changeability requirements under increasing complexity constraints. Sensors are the ears and eyes of systems controlling automated production processes. Additionally, they provide necessary information to monitoring and maintenance applications which are also forced to be adaptable to system evolution. Semantic technologies are key enabler to foster integration and interoperability of large-scale, heterogeneous data sources. In this paper, we discuss approaches to integrate semantic technology with the upcoming standard OPC-UA and present a solution to taps the full potential of both through semantic-based sensor discovery and automatic sensor data provisioning with OPC-UA.

Flexible plant components can significantly increase the flexibility of manufacturing systems and enable concepts like mass-customized production. This paper presents an approach for production planning and execution for flexible manufacturing system components, based on software agents. The agents are implemented directly on a PLC, making them capable of real-time operation. Additionally, a service-interface contributes to the vertical integration of the approach into the higher level planning of a flexible production system. Using the presented software agents, flexible plant components can be fully automated and integrated in modern FMS, leading to a higher degree of flexibility and dependability of the overall system. The approach is evaluated on a flexible fixture device as a FMS component, capable of automatic reconfiguration.

Our goal is to improve the handling of new possibilities given through object-oriented extension of the IEC 61131-3 programming techniques. We already know that modeling of structure is a challenge for automation engineers and often neglected. In an interdisciplinary research team we examined whether the modeling order with the Unified Modeling Language (UML) class diagram for the structural aspect and the UML state chart diagram for the behavioral aspects cause significant differences in model quality. Additionally, often occurring errors were analyzed to find hindering factors in modeling and to learn what was misunderstood. Therefore we combine usability aspects, notational and procedural aspects in PLC programming with learning and educational aspects to find how education and application of modeling notations could be improved.

During the last eight years we investigated the benefit of modeling notations, e.g. UML and domain specific UML profiles as well as specialized modeling languages (Idiomatic Control Language - ICL) in software engineering (IEC 61131-3) in machine and plant automation with three different experiments and approximately more than 120 participants. The results of the conducted experiments revealed the benefits and the shortcomings of UML in the field of supporting control programming. Furthermore we can show the impact of different modeling notations on software quality (IEC 61131-3) and time consumption for the software development process as well as some results on how to evaluate model driven engineering under usability aspects.

Specification of Manufacturing Execution Systems (MES) as software systems related to the technical process represents a challenge regarding interdisciplinary communication. In this paper modeling notations currently applied, like the Businesses Process Model and Notation (BPMN), Petri Nets or the Unified Modeling Language (UML) were analyzed on the basis of standardized and MES specific requirements. Using these criteria, we show that so far, no graphical modeling notation supports this specification process adequately. In the presented approach “SpeziMES” the identified gaps will be closed by developing a graphical MES modeling framework based on the BPMN 2.0. Main contribution of this framework is the integration of a technical system model, a production process model and an MES functional model with their interconnections and dependencies.

Within the maintenance of the IEC 61131-3 standard, its extension towards object orientation (OO) is currently discussed and very likely to happen. Such an extension would enable the bidirectional mapping between OO-IEC 61131-3 software structures and graphical UML class diagrams (ISO/IEC 19501). This paper presents the main OO-extension to IEC 61131-3. This is followed by a specification of bidirectional mapping rules to UML class diagrams which allow the seamless integration of UML and OO-IEC 61131-3. Finally this paper presents exemplarily a domain/specific design pattern for the implementation of a modular machine mode control according to IEC 61512. Within a research project a class diagram editor and the corresponding mapping rules were prototypically implemented and evaluated in CoDeSys V3.

Operator training without a process model is necessary in certain plant manufacturing industry's applications because the process models from the control theory perspective are still not available. This paper describes an experiment that analyzed the benefits of 3D data visualization integrated in HMI for process control in combination with different types of operator training to obtain process knowledge. The benefits of interactive 3D slider training in identifying errors during operation could be proven.

The different aspects that need to be tackled in order to develop automation for machines and plants through reusing tested modules will be discussed. The development of such modules involves all disciplines and their specific variations and version management, as well as the dependencies between those variations. Version management is needed to track modifications of these different modules. Variations for these modules exist due to the different functions and versions of modules. Maintenance and feedback to these modules have to be supported through out the whole life cycle, from planning to design, development, implementation, operation and maintenance. The paper presents an example for the dependencies mentioned above, the existing solutions, the problems faced and methods to better support these dependencies.