Protective relays are integral to the reliability of any electrical power system, and are fundamental to the decision-making of their protection systems. They support the detection and isolation of problems in the power system, so that the operation of unaffected parts can be maintained. ElectroMechanical Relays (EMRs) are still predominant around the globe in the high and extra high voltage transmission systems. Thus, ensuring the reliability and traceability of relays is of major importance. One way to achieve this is through parallel redundancy by implementing redundant sensor architectures, such as one-out-of-two (1oo2). In this paper, we propose a novel algorithm for the fault prognosis-i.e., detection and failure date prediction – and isolation prediction for redundant 1002 architectures. The algorithm predicts the failure ahead of time and provides an estimated date for the failure event. Our contribution in this work is on the fault isolation prediction, where we infer-ahead of time, before the occurrence of the fault-which relay of the pair will cause the failure. The fault isolation is achieved by means of Machine Learning (ML) based feature extraction and binary classification methods. We apply the algorithm on EMRs based solely on the discrepancy time signals of the opening and closing events of the relays. The algorithm has been tested on data from redundant EMRs from the publicly available SOReDD dataset. While relays are binary switches, our work could potentially not only be applied to other types of binary switches but also to binary sensors as they also produce a binary output signal.

Globalization in the field of industry is fostering the need for cognitive production systems. To implement modern concepts that enable tools and systems for such a cognitive production system, several challenges on the shop floor level must first be resolved. This paper discusses the implementation of selected cognitive technologies on a real industrial case-study of a construction machine manufacturer. The partner company works on the concept of mass customization but utilizes manual labour for the high-variety assembly stations or lines. Sensing and guidance devices are used to provide information to the worker and also retrieve and monitor the working, with respecting data privacy policies. Next, a specified process of data contextualization, visual analytics, and causal discovery is used to extract useful information from the retrieved data via sensors. Communications and safety systems are explained further to complete the loop of implementation of cognitive entities on a manual assembly line. This deepened involvement of cognitive technologies are human-centered, rather than automated systems. The explained cognitive technologies enhance human interaction with the processes and ease the production methods. These concepts form a quintessential vision for an effective assembly line. This paper revolutionizes the existing industry 4.0 with an even-intensified human–machine interaction and moving towards cognitivity.

Safety-critical automation often requires redundancy to enable reliable system operation. In the context of integrating sensors into such systems, the one-out-of-two (1oo2) sensor architecture is one of the common used methods used to ensure the reliability and traceability of sensor readings. In taking such an approach, readings from two redundant sensors are continuously checked and compared. As soon as the discrepancy between two redundant lines deviates by a certain threshold, the 1oo2 voter (comparator) assumes that there is a fault in the system and immediately activates the safe state. In this work, we propose a novel fault prognosis algorithm based on the discrepancy signal. We analyzed the discrepancy changes in the 1oo2 sensor configuration caused by degradation processes. Several publicly available databases were checked, and the discrepancy between redundant sensors was analyzed. An initial analysis showed that the discrepancy between sensor values changes (increases or decreases) over time. To detect an increase or decrease in discrepancy data, two trend detection methods are suggested, and the evaluation of their performance is presented. Moreover, several models were trained on the discrepancy data. The models were then compared to determine which of the models can be best used to describe the dynamics of the discrepancy changes. In addition, the best-fitting models were used to predict the future behavior of the discrepancy and to detect if, and when, the discrepancy in sensor readings will reach a critical point. Based on the prediction of the failure date, the customer can schedule the maintenance system accordingly and prevent its entry into the safe state—or being shut down.

The proliferation of the industrial digitisation through paradigms such as Industry 4.0 or Industrial Internet of Things have created a more complex work environment for human workers. Even though robots and machines have been created to make workers’ jobs easier and less stressful, work-related stress is still present in industrial environments. In addition to the traditional stressors such as workload or strict deadlines, the constant demand for interaction with robots and machines and the poorly designed and unclear interfaces lead to additional cognitive stress for workers. In this poster, we outline a system based on wearable sensors that can collect and store data relevant to the assessment of workers’ stress. The sensors measure physiological indicators such as heart rate or respiratory rate, as well as geospatial data that includes GPS coordinates and workers’ movement, speed and acceleration. The data generated by the sensors is transmitted to the lightweight on-board unit on the back of the fabrics (T-shirt or bra) and then streamed to the processing unit (Raspberry Pi) via BLE. The collected data will be processed in order to describe the relationships between the stressors and the workers’ health status and behavior.

—The number of Commercial-Off-The-Shelf (COTS) microprocessors and microcontrollers used in safety applications increased significantly over the last decade. In contrast to safety-certified microcontrollers, these microcontrollers are produced without integrated protection against memory soft errors, and limited in terms of available memory and computation power. However, due to the constant optimizations of the memory’s physical size and the voltage margins, the probability that external factors, such as magnetic fields or cosmic rays, temporally alter a memory state (and thus cause a soft error) rises. Especially within safety-critical automation systems, it is crucial to address such errors and a wide range of error mitigation strategies have been proposed. In the context of established brownfield automation systems, the redesign and deployment of new hardware is usually not feasible. Therefore software-based strategies are required, which can be deployed on existing fail-safe architectures to further improve their performances, without requiring their rework or conceptual changes. This article identifies challenges associated with software-based soft error detection and correction strategies. Along with the challenges, a short overview of currently applicable software-based mitigation strategies is given and the strategies are evaluated.

The next step for companies towards smart manufacturing is to process the previously integrated and contextualised data to create manufacturing intelligence. This poster presents a use case for enhancing self-awareness, contextual-awareness, and peer-to-peer awareness to a Programmable Logic Controller (PLC) by using low-cost IoT equipment. An experimental setup is demonstrated investigating enhanced awareness features for the PLC making use of internal and external temperature measurements.

As memories are becoming a ubiquitous and indispensable part of electronic devices across all industrial domains, the importance of their reliability and fault-tolerance increases. This especially holds for safety-critical applications, which exhibit different levels of data criticality. As a consequence, recent research aims to proactively engage environmentally induced soft errors, by developing new methods for error detection, mitigation, and data recovery in the mixed-critical memories. This article presents a flexible soft error correction strategy called Redundant Parity (RP), designed to enhance existing 1oo2 architectures. RP extends a 1oo2 system's ability of fault detection by enabling the recovery of faulty data utilizing the parity bit concept. An initial evaluation of the strategy in terms of its runtime performance and memory overhead is performed and compared with other software-based mitigation strategies. The preliminary results suggest that RP is indeed a suitable soft error mitigation strategy in existing 1oo2 fail-safe systems.

This paper explores how the functional safety of industrial deployments can be improved through emerging Industrie 4.0 approaches. We discuss how new sources of data, that are becoming accessible through advancing digitalization, can be used for this purpose, and how principles from predictive maintenance systems can be applied to industrial fail-safe applications: based on data from the industrial components themselves and from their environment as well as on metadata about interactions between these systems and people, we propose to create a model-based monitoring and controlling system that focuses on preserving the functional safety of the installation as a whole. We expect such a Predictive Fail-Safe system to mitigate or even prevent unsafe consequences of failures even in highly dynamic “smart factories”, thereby reducing or preventing harm to other equipment, the environment, and the involved people.