DECENTRALIZED AUTONOMOUS FAULT DETECTION IN WIRELESS STRUCTURAL HEALTH MONITORING SYSTEMS USING STRUCTURAL RESPONSE DATA

Sensor faults can affect the dependability and the accuracy of structural health monitoring (SHM) systems. Recent studies demonstrate that artificial neural networks can be used to detect sensor faults. In this paper, decentralized artificial neural networks (ANNs) are applied for autonomous sensor fault detection. On each sensor node of a wireless SHM system, an ANN is implemented to measure and to process structural response data. Structural response data is predicted by each sensor node based on correlations between adjacent sensor nodes and on redundancies inherent in the SHM system. Evaluating the deviations (or residuals) between measured and predicted data, sensor faults are autonomously detected by the wireless sensor nodes in a fully decentralized manner. A prototype SHM system implemented in this study, which is capable of decentralized autonomous sensor fault detection, is validated in laboratory experiments through simulated sensor faults. Several topologies and modes of operation of the embedded ANNs are investigated with respect to the dependability and the accuracy of the fault detection approach. In conclusion, the prototype SHM system is able to accurately detect sensor faults, demonstrating that neural networks, processing decentralized structural response data, facilitate autonomous fault detection, thus increasing the dependability and the accuracy of structural health monitoring systems

DECENTRALIZED AUTONOMOUS FAULT DETECTION IN WIRELESS STRUCTURAL HEALTH MONITORING SYSTEMS USING STRUCTURAL RESPONSE DATA

Sensor faults can affect the dependability and the accuracy of structural health monitoring (SHM) systems. Recent studies demonstrate that artificial neural networks can be used to detect sensor faults. In this paper, decentralized artificial neural networks (ANNs) are applied for autonomous sensor fault detection. On each sensor node of a wireless SHM system, an ANN is implemented to measure and to process structural response data. Structural response data is predicted by each sensor node based on correlations between adjacent sensor nodes and on redundancies inherent in the SHM system. Evaluating the deviations (or residuals) between measured and predicted data, sensor faults are autonomously detected by the wireless sensor nodes in a fully decentralized manner. A prototype SHM system implemented in this study, which is capable of decentralized autonomous sensor fault detection, is validated in laboratory experiments through simulated sensor faults. Several topologies and modes of operation of the embedded ANNs are investigated with respect to the dependability and the accuracy of the fault detection approach. In conclusion, the prototype SHM system is able to accurately detect sensor faults, demonstrating that neural networks, processing decentralized structural response data, facilitate autonomous fault detection, thus increasing the dependability and the accuracy of structural health monitoring systems

Proceedings of the 29th EG-ICE International Workshop on Intelligent Computing in Engineering

This publication is the Proceedings of the 29th EG-ICE International Workshop on Intelligent Computing in Engineering from July 6-8, 2022. The EG-ICE International Workshop on Intelligent Computing in Engineering brings together international experts working on the interface between advanced computing and modern engineering challenges. Many engineering tasks require open-world resolution of challenges such as supporting multi-actor collaboration, coping with approximate models, providing effective engineer-computer interaction, search in multi-dimensional solution spaces, accommodating uncertainty, including specialist domain knowledge, performing sensor-data interpretation and dealing with incomplete knowledge. While results from computer science provide much initial support for resolution, adaptation is unavoidable and most importantly, feedback from addressing engineering challenges drives fundamental computer-science research. Competence and knowledge transfer goes both ways. &nbsp

Proceedings of the 29th EG-ICE International Workshop on Intelligent Computing in Engineering

This publication is the Proceedings of the 29th EG-ICE International Workshop on Intelligent Computing in Engineering from July 6-8, 2022. The EG-ICE International Workshop on Intelligent Computing in Engineering brings together international experts working on the interface between advanced computing and modern engineering challenges. Many engineering tasks require open-world resolution of challenges such as supporting multi-actor collaboration, coping with approximate models, providing effective engineer-computer interaction, search in multi-dimensional solution spaces, accommodating uncertainty, including specialist domain knowledge, performing sensor-data interpretation and dealing with incomplete knowledge. While results from computer science provide much initial support for resolution, adaptation is unavoidable and most importantly, feedback from addressing engineering challenges drives fundamental computer-science research. Competence and knowledge transfer goes both ways. &nbsp

Advancing civil infrastructure assessment through robotic fleets

Modern civil engineering structures, instrumented with Internet-of-Things-enabled smart sensors and actuators, are considered cyber-physical systems that integrate physical processes with computational and communication elements. This short communication aims to portray a milestone in the field of monitoring and inspection of civil infrastructure, collaboratively conducted by autonomous, robotic devices orchestrated in robotic fleets. It is expected that robot-based civil infrastructure assessment will revolutionize structural maintenance of the deteriorating building stock, which is increasingly exacerbated by the effects of climate change and develops into a major societal challenge

Mobile Structural Health Monitoring Based on Legged Robots

With the advancements in information, communication, and sensing technologies, structural health monitoring (SHM) has matured into a substantial pillar of infrastructure maintenance. In particular, wireless sensor networks have gradually been incorporated into SHM, leveraging new opportunities towards reduced installation efforts and enhanced flexibility and scalability, as compared to cable-based SHM systems. However, wireless sensor nodes are installed at fixed locations and need to be employed at high density to reliably monitor large infrastructure, which may cause high installation costs. Furthermore, the limited power autonomy of wireless sensor networks, installed at fixed locations for unattended long-term operation, still represents a significant constraint when deploying stationary wireless sensor nodes for SHM. To resolve the critical constraints stemming from costly high-density deployment and limited power autonomy, a mobile structural health monitoring concept based on legged robots is proposed in the study reported in this paper. The study explores the accuracy and cost-efficiency of deploying legged robots in dense measurement setups for wireless SHM of civil infrastructure, aiming to gain insights into the advantages of mobile wireless sensor nodes in general and of legged robots in particular, in terms of obtaining rich information on the structural condition. As is shown in this paper, the legged robots, as compared to stationary wireless sensor nodes, require a smaller number of nodes to be deployed in civil infrastructure to achieve rich sensor information, entailing more cost-efficient, yet accurate, SHM. In conclusion, this study represents a first step towards autonomous robotic fleets advancing structural health monitoring

Adaptive Fehlerdiagnose bei gleichzeitigen Sensorfehlern in Bauwerksmonitoringsystemen

Sensorfehler in Bauwerksmonitoringsystemen können zum Verlust von Daten und zu mangelhaften Zustandsbewertungen und Lebensdauerabschätzungen führen, wobei im schlimmsten Fall strukturelle Schäden unentdeckt bleiben. Infolgedessen finden Fehlerdiagnosekonzepte zunehmend Anwendung in Bauwerksmonitoringsystemen. Die meisten Fehlerdiagnosekonzepte für Bauwerksmonitoringsysteme erfassen jedoch nur das Auftreten eines einzelnen Fehlers und berücksichtigen nicht das gleichzeitige Auftreten mehrerer Fehler in mehreren Sensoren, wie es in realen Bauwerksmonitoringsystemen vorkommen kann. In diesem Beitrag wird ein adaptiver Ansatz zur Fehlerdiagnose für Bauwerksmonitoringsysteme vorgestellt, der das gleichzeitige Auftreten von Fehlern bei mehreren Sensoren berücksichtigt. Der adaptive Ansatz umfasst Fehlerdetektion, -isolation und -behebung. Der Ansatz baut auf analytischer Redundanz auf, die korrelierte Daten von mehreren Sensoren eines Bauwerksmonitoringsystems verwendet. Insbesondere werden Fehler mithilfe der Prognosen von künstlichen neuronalen Netzen (KNN), die Korrelationen innerhalb der Sensordaten nutzen, diagnostiziert. Der vorgeschlagene Ansatz zur adaptiven Fehlerdiagnose bei gleichzeitigen Sensorfehlern in Bauwerksmonitoringsysteme wurde anhand von Sensordaten einer Eisenbahnbrücke validiert. Die Ergebnisse zeigen, dass der vorgeschlagene Ansatz die Zuverlässigkeit von Bauwerksmonitoringsystemen verbessert

Entwicklung eines kostengünstigen, IoT-fähigen Shaketable-Systems

Shaketables sind nützliche Hilfsmittel zur Validierung von Bauwerksmonitoringsystemen. Kommerziell erhältliche Shaketables sind jedoch meist teuer und für Forschungs- und Lehreinrichtungen oft kaum finanzierbar. Darüber hinaus führt der zunehmende Online-Unterricht, der durch die COVID-19-Pandemie stärker in den Vordergrund trat, dazu, dass Forschende und Studierende zur Durchführung von Experimenten nicht physisch in Labore kommen können. Deshalb wird in diesem Beitrag die Implementierung und Validierung eines kostengünstigen, Internet of Things (IoT)-fähigen, Shaketable-Systems (IoT-STS) vorgestellt. Das IoT-STS wurde aus handelsüblichen Komponenten zusammengebaut und enthält eine mobile App, um Verschiebungsdaten über einen IoT-basierten Cloud-Service an einen Mikroprozessor zu senden, der über Aktuatoren Schwingungen auf den Shaketable ausübt. Umgekehrt sendet der Mikroprozessor die ausgegebenen Verschiebungsdaten, die die vom Shaketable reproduzierte Bewegung repräsentieren, an die Cloud für einen Fernzugriff und die Visualisierung. Das IoT-STS wird durch zwei Tests validiert, (1) in einem Test mit periodischen Schwingungen und (2) mit seismischen Verschiebungsdaten. Die Ergebnisse zeigen, dass das entwickelte IoT-STS Schwingungen präzise auf Teststrukturen übertragen kann, per Fernzugriff nutzbar ist und somit insbesondere vor dem Hintergrund des seit der COVID-19-Pandemie zugenommenen Online-Unterrichts ein wertvoller Mehrwert ist