Fault prediction methods aim for the realtime monitoring and detection of equipment failures in Industry 4.0 environments, with the integration of the internet of things and advanced analytics. The utilization of advanced, data-driven methods such as semantic technologies and ontology-based reasoning can enhance the efficiency of the development, operation, and integration of fault diagnostic systems and the management of related information. The main contribution of this paper is to present an ontology-based fault detection and diagnosis framework to improve the efficiency of fault analysis and detection processes in industrial systems. The framework uses ontologies to represent the complex relationships between different types of data, maintenance events, or equipment failures, and not only relays on standard failure analysis methods but adapts fault tree analysis for semantic reasoning. Additionally, a conceptual domain ontology is proposed, which provides efficient data interoperability in the complex system. An illustrative, wire harness manufacturing-based example presents, that the framework can identify potential failure modes and their causes, and perform anomaly detection and root cause analysis, using the domain ontology with reasoning techniques.

Background:Human workers are indispensable in the human–cyber-physical system in the forthcoming Industry 5.0. As inappropriate work content induces stress and harmful effects on human performance, engineering applications search for a physiological indicator for monitoring the well-being state of workers during work; thus, the work content can be modified accordingly. The primary aim of this study is to assess whether heart rate variability (HRV) can be a valid and reliable indicator of acute work-content-related stress (AWCRS) in real time during industrial work. Second, we aim to provide a broader scope of HRV usage as a stress indicator in this context.Methods:A search was conducted in Scopus, IEEE Xplore, PubMed, and Web of Science between 1 January 2000 and 1 June 2022. Eligible articles are analyzed regarding study design, population, assessment of AWCRS, and its association with HRV.Results:A total of 14 studies met the inclusion criteria. No randomized control trial (RCT) was conducted to assess the association between AWCRS and HRV. Five observational studies were performed. Both AWCRS and HRV were measured in nine further studies, but their associations were not analyzed. Results suggest that HRV does not fully reflect the AWCRS during work, and it is problematic to measure the effect of AWCRS on HRV in the real manufacturing environment. The evidence is insufficient for a reliable conclusion about the HRV diagnostic role as an indicator of human worker status.Conclusion:This review is valuable in the Operator 4.0 paradigm, calling for more trials to validate the use of HRV to measure AWCRS on human workers.

In partially automated manufacturing, humans work together with mobile robots. Trajectory prediction, i.e. predicting future positions of human workers, improves collaboration and coexistence between humans and robots on the shop floor. In this paper, we discuss the interrelated research questions of how human motion trajectories can be predicted and how mobile robots such as Autonomous Mobile Robots and Automated Guided Vehicles can take such predictions into account in their pathfinding and navigation. On the robot side, advanced D* pathfinding algorithms allow robots to take dynamic obstacles into account. For trajectory prediction, the position of human workers is determined by an Ultra-Wideband-based Real-Time Locating System. A trajectory prediction framework is introduced to support the implementation and use of pattern- and planning-based trajectory prediction algorithms. The evaluation is based on scenarios from the addressed problem area of manufacturing.

With the increasing importance of highly connected and monitored processes supported by industrial information systems, such as knowledge graphs, the integration of the operator has become urgent due to its high cost and is also to be appreciated from a social point of view. The facilitation of collaboration between humans and machines is a fundamental target for Industrial Cyber-Physical Systems, as the workforce is the most agile and flexible manufacturing resource. Furthermore, the design of such a framework requires effective systems to utilise resources and information. This paper aims to provide recommendations of ontologies and standards that can support monitoring work conditions, scheduling, planning and supporting the operator and the possibilities to formalise the classic work instructions to analyse the unique activities. The main contributions of the work are that it proposes a design work-frame of a knowledge graph where the work performed by the operator is in the scope, including the evaluation of movements, collaboration with machines, work steps, ergonomics and other conditions. The paper highlights that activity recognition technologies can enhance the utilisable data in a knowledge graph for a smart factory. With this approach, the future goal may be to automate the entire data collection and knowledge exploration processes, which can facilitate the support of the human-digital twin and the implementation of augmented reality technologies in the Industry 5.0 concept.

Manufacturing companies are facing two major trends affecting their business operations: "automatization" and "collaboration". Companies have realized that they still need humans on the shop floor beside the availability of high levels of automation solutions in the market. This realization has created a new Industrial Revolution known as "Industry 5.0". While the primary concern in Industry 4.0 is about achieving high levels of full automation, Industry 5.0 focuses on creating synergies between humans and autonomous machines in semi-automated manufacturing systems toward flexible, resilient, and sustainable systems. The critical element of human-automation synergies is a better understanding of the excellent cooperation between humans and making a better collaboration between humans and autonomous machines inspired by it. The proposed Intelligent Collaborative Manufacturing Space (ICMS) aims to create a framework for supporting collaborations based on smart sensor networks and data science techniques. Four main elements or sub-spaces characterize this "Intelligent Workspace": (i) the Working Space, (ii) the Monitoring Space, (iii) the Modelling Space, and (iv) the Decision Space. The ICMS is a framework envisioned for supporting the effective collaboration between humans and automated and semi-automated production assets based on activity recognition and prediction paired with machine learning optimization algorithms. A methodology for developing ICMSs is described in detail in this paper.

Flexible intralogistics systems use automated guided vehicles (AGV) to transport goods. In assembly and warehouses, AGVs and human workers often work side by side. For optimal navigation, AGVs must consider human movement and estimate future positions of workers. Using real-time locating systems (RTLS) to improve human-robot collaboration enables more energy-efficient and safer AGV wayfinding strategies. This paper gives a summary on the topics RTLS, AGV wayfinding and trajectory prediction and introduces the momentum-based approach to predicting future worker positions in factories and warehouses. The results show that ultra-wideband-based RTLS are very well suited for trajectory prediction in the production sector.