Electric motors account for a substantial portion of electricity usage in industrial sectors. Significant energy savings can be achieved by using high-efficiency motors and Variable Speed Drives (VSDs). However, to maximize energy efficiency, it is crucial to ensure motors are properly sized, as even VSDs cannot compensate for the inefficiencies of incorrectly sized motors. This paper tackles the issue of motor sizing, which requires a detailed evaluation of operational losses and the selection of appropriate motor specifications. To address this, we introduce a framework that integrates various motor loss prediction models, facilitates comprehensive energy efficiency assessment, and offers recommendations for motor specifications that meet the operational requirements. The framework aims to improve energy efficiency, lower operational costs, and promote sustainable operations in industrial environments.

Timing prediction for real-time systems, especially those based on multi-core processor technology, presents enor-mous challenges in the design of modern industrial control sys-tems. Classical timing analysis techniques that have been effective in the past cannot yet keep up with the increased complexity of industrial control systems. Therefore, using hardware-in-the-loop testing to understand the timing behavior of real-time tasks is a prevalent practice across many industries. However, applying this state-of-the-practice to Continuous Integration (CI) software development workflows is expensive, and frequently leads to delayed developer feedback on task timing for code commits. To address this challenge, we propose the Chronos framework, which focuses on improving development efficiency of industrial control system software. Chronos utilizes CI data from both simulation and hardware-in-the-loop testing to build machine learning based, cross-platform timing prediction models, which correlate the simulated performance of the tasks with their actual timing observed on the target embedded hardware. For any new code that is committed, the timing prediction is triggered by the CI server with the trained machine learning models, enabling fast feedback on timing behavior of the committed code. We demonstrate the effectiveness of Chronos with preliminary results on real industrial control system setups.

Validation of conformance to cybersecurity standards for industrial automation and control systems is an expensive and time consuming process which can delay the time to market. It is therefore crucial to introduce conformance validation stages into the continuous integration/continuous delivery pipeline of products. However, designing such conformance validation in an automated fashion is a highly non-trivial task that requires expert knowledge and depends upon available security tools, ease of integration into the DevOps pipeline, as well as support for IT and OT interfaces and protocols.This paper addresses the aforementioned problem focusing on the automated validation of ISA/IEC 62443-4-2 standard component requirements. We present an extensive qualitative analysis of the standard requirements and the current tooling landscape to perform validation. Our analysis demonstrates the coverage established by the currently available tools and sheds light on current gaps to achieve full automation and coverage. Furthermore, we showcase for every component requirement where in the CI/CD pipeline stage it is recommended to test it and the tools to do so.

Machine-learning-based approaches have emerged as viable solutions for automatic detection of container-related cyber attacks. Choosing the best anomaly detection algorithms to identify such cyber attacks can be difficult in practice, and it becomes even more difficult for zero-day attacks for which no prior attack data has been labeled. In this paper, we aim to address this issue by adopting an ensemble learning strategy: a combination of different base anomaly detectors built using conventional machine learning algorithms. The learning strategy provides a highly accurate zero-day container attack detection. We first architect a testbed to facilitate data collection and storage, model training and inference. We then perform two case studies of cyber attacks. We show that, for both case studies, despite the fact that individual base detector performance varies greatly between model types and model hyperparameters, the ensemble learning can consistently produce detection results that are close to the best base anomaly detectors. Additionally, we demonstrate that the detection performance of the resulting ensemble models is on average comparable to the best-performing deep learning anomaly detection approaches, but with much higher robustness, shorter training time, and much less training data. This makes the ensemble learning approach very appealing for practical real-time cyber attack detection scenarios with limited training data.

This paper proposes a real-time power system simulation framework that is capable of simulating steady state and electromechanical transients of power systems, with sub-millisecond time resolution. The framework can integrate power system component models packed as reusable Functional Mockup Units (FMUs) to flexibly create power system simulations without the need to recreate new models for different power systems. The integration of individual components is based on a novel model decomposition method, which enables the FMU reuse in different system contexts, as well as a parallel simulation execution onto multi-core machines. Furthermore, the paper proposes methods to optimize the allocation of components to cores and shows that the framework can simulate a medium voltage distributed electrical grid of about 20 components in real-time on a commodity multi-core machine.

Enterprises in the field of industrial automation experience an increasing demand for providing virtualized software solutions. Inspired by the recent trends in serverless and cloud computing, software virtualization is considered even for safety-critical applications with hard real-time requirements, as a means of avoiding hardware vendor lock-in and reducing volume and maintenance cost of devices. In this work, we evaluate the applicability of OS-level virtualization to an industrial automation use case. Our application runs in Docker containers on top of Linux patched with PREEMPT_RT. We investigate the ability of Docker coupled with diverse networking technologies to fulfill the latency requirements of the application under normal or heavy system load. We empirically compare four networking technologies with respect to communication latency and frequency of missing packets. The results indicate that Docker with certain technologies, such as the Single Root I/O Virtualization interface, performs robustly even under heavy load, enabling sufficient performance isolation and low overhead that does not jeopardise the real-time performance of our application.

Testing of large and complex industrial control systems is challenging as the space of possible input and environmental parameters is large. Searching the entire space for potential failures is practically infeasible. This paper introduces an industrial control system robustness testing problem and evaluates artificial intelligence (AI) based strategies to efficiently explore the space and to identify parameter sets that can cause the system to fail. The proposed solution approach uses regression techniques to speed up the search and clustering methods to identify parameter sets that represent distinct system failures.

The concepts of decision forces and the decision forces viewpoint were proposed to help software architects to make architectural decisions more transparent and the documentation of their rationales more explicit. However, practical experience reports and guidelines on how to use the viewpoint in typical industrial project setups are not available. Existing works mainly focus on basic tool support for the documentation of the viewpoint or show how forces can be used as part of focused architecture review sessions. With this paper, we share experiences and lessons learned from applying the decision forces viewpoint in a distributed industrial project setup, which involves consultants supporting architects during the re-design process of an existing large software system. Alongside our findings, we describe new forces that can serve as template for similar projects, discuss challenges applying them in a distributed consultancy project, and share ideas for potential extensions.

Hardware-in-the-loop (HIL) testing is an integral part of the development of power system automation solutions. HIL test beds facilitate system-level testing and validation of controllers against their specifications. The information required for HIL test bed configurations is typically scattered across different tools used in the development of power system automation solutions. These tools often have different formats and naming schemes. This implies that a significant effort is required for consolidating data from the tools and for creating valid test configurations from the consolidated data. This paper presents a Model-Driven Engineering (MDE) approach for creating HIL test configurations in a tool-, platform-, and protocol-agnostic manner. It shows how the MDE approach can be applied in practice to derive test configurations based on the information originating from different tools in a semi-automated fashion.

Trends in industrial automation systems are placing more importance on using streams of digitized data to perform various automation functions in real-time, e.g., power, process, and factory automation. To ensure high reliability and availability, individual devices or (sub-)systems thereof need to be tested with respect to their expected real-time behavior in the system context at various stages during product and project life cycle. In this paper, we introduce a real-time streaming platform called MARS that provides the means to monitor (M) high frequency data streams, analyze (A) them with respect to their properties, record data traffic (R) based on user-specified rules, and simulate data traffic (S) and in general the behavior of certain functions of one or more devices according to highly customizable scenarios. We present the design principles of MARS, the real-time software architecture, and evaluation results.

The process bus communication in substations facilitates the exchange of data from multiple bays to different bay level and station level devices. This enables the realization of applications that depend on data from multiple bays, e.g. Central Synchrocheck. Such applications have high availability and hard real-time requirements. In this paper, different process bus topology variants using the Parallel Redundancy Protocol (PRP) and High-availability Seamless Ring (HSR) for Central Synchrocheck application are investigated. The performance of these topology variants is analyzed under worst-case communication loads for different sizes of substations. To this end, simulation models for Merging Units, Central Synchrocheck device, PRP and HSR network elements are developed in OMNEST. These models are used to build different network topologies. The network topologies are simulated to gather the end-to-end latency statistics for GOOSE and SV messages. The simulation analysis distinguishes the topology variants that offer better performance for a certain substation size and also indicates for which process bus network segments Gigabit Ethernet is appropriate.

Industrial control systems are often large and complex distributed systems and therefore expose a large potential attack surface. Effectively minimizing this attack surface requires security experts and significant manpower during engineering and maintenance of the system. This task, which is already difficult for today's control systems, will become significantly more complex for tomorrow's systems, which can reconfigure themselves dynamically, e.g., if hardware failures occur. In this article, we present a dynamic security system which can automatically minimize the attack surface of a control system's communication network. This security system is specifically designed for next-generation industrial control systems, but can also be applied in current generation systems. The presented security system adapts the necessary parameters of network and security controls according to the underlying changes in the control system environment. This ensures a better cyber security resilience against system compromise and reduces the attack surface because security controls will only allow data transfer that is required by the control application. Our evaluations for a next generation industrial control system and a current generation substation automation system show that the attack surface can be reduced by up to 90%, depending on the size and actual configuration of the control system.

There are two types of real-time Layer 2 multicast traffic in an IEC 61850 substation. It is desirable to properly segment the multicast traffic in order to avoid overloading end devices that do not subscribe to the traffic. The traffic can be segmented based on VLANs and multicast address filters. However, the engineering of VLANs and multicast address filters in complex IEC 61850 substation systems requires meticulous effort to avoid errors. This paper presents a systematic approach to not only assign minimum number of VLANs and multicast addresses to real-time traffic in an IEC 61850 substation, but also to derive the appropriate VLAN and multicast address filter settings for all Ethernet switches in the substation. The derived switch filter settings ensure that the traffic reaches only its subscribers and thereby the proper operation of substation automation system.

Many airlines envision the deployment of wireless LAN infrastructure for video streaming to inflight entertainment devices attached to cabin seats. The sheer advantage of wireless LAN infrastructure is that it facilitates flexible configuration of cabin seat layout with less effort and low cost. The emerging IEEE 802.11n technology which has high throughput is an interesting option for such wireless LAN infrastructure. This paper evaluates the performance of video streaming over IEEE 802.11n, considering the QoS requirements of inflight video, by ns-2 simulation.

Many distributed real-time embedded systems often perform critical functions, which implicitly require high reliability. Consequently, the application designer for a distributed real-time embedded system is confronted with a challenge of deriving a reliable schedule for a real-time application in the presence of individual component and common cause failures of the system. This paper proposes a formal framework for reliable scheduling of a real-time application over a distributed realtime embedded system considering both failures. The framework derives an optimal reliable schedule for the real-time application, while not violating the constraints of the application and the system resources.

In a networked embedded system a real-time application is distributed and scheduled over a set of nodes, which are connected by means of heterogeneous networks. The application designer for such a system is often confronted with the challenge of providing better response time and low failure probability for the distributed application. These objectives are conflicting with each other hence some trade-off between these two design objectives is needed to ensure a satisfactory application schedule. More often the designer's satisfaction can be expressed as a set of aspiration bounds on the design objectives, that if attained will be a good schedule. This paper proposes a satisfying multiobjective mixed integer programming (MOMIP) framework for deriving such a schedule

Timed automata provide useful state machine based representations for the validation and verification of realtime control systems. This paper introduces an algorithmic methodology to translate the state space visualization of a centralized real-time control system to a decentralized one. Given a set of timed automata representing a centralized real-time control system, the algorithm partitions them into a collection of interacting submachines. Importantly, this methodology allows for model-checking of the derived decentralized system against the same set of verifications as that specified for the centralized system. The complexity analysis of the algorithm is presented as a function of the number of tasks and nodes comprising the decentralized system.