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 andSensor 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.…
Dokumentart: | Konferenzveröffentlichung |
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Verfasserangaben: | Katrin Jahr, Robert Schlich, Kosmas Dragos, Kay SmarslyGND |
DOI (Zitierlink): | https://doi.org/10.25643/bauhaus-universitaet.2803Zitierlink |
URN (Zitierlink): | https://nbn-resolving.org/urn:nbn:de:gbv:wim2-20170314-28031Zitierlink |
ISSN: | 1611-4086 |
Titel des übergeordneten Werkes (Englisch): | Digital Proceedings, International Conference on the Applications of Computer Science and Mathematics in Architecture and Civil Engineering : July 20 - 22 2015, Bauhaus-University Weimar |
Herausgeber: | Klaus GürlebeckGND, Tom LahmerORCiDGND |
Sprache: | Englisch |
Datum der Veröffentlichung (online): | 03.03.2017 |
Datum der Erstveröffentlichung: | 28.08.2015 |
Datum der Freischaltung: | 14.03.2017 |
Veröffentlichende Institution: | Bauhaus-Universität Weimar |
Urhebende Körperschaft: | Bauhaus-Universität Weimar |
Institute und Partnereinrichtugen: | Fakultät Bauingenieurwesen / Professur Angewandte Mathematik |
Seitenzahl: | 8 |
Freies Schlagwort / Tag: | Data, information and knowledge modeling in civil engineering; Function theoretic methods and PDE in engineering sciences; Mathematical methods for (robotics and) computer vision; Numerical modeling in engineering; Optimization in engineering applications |
GND-Schlagwort: | Angewandte Informatik; Angewandte Mathematik; Building Information Modeling; Computerunterstütztes Verfahren |
DDC-Klassifikation: | 000 Informatik, Informationswissenschaft, allgemeine Werke / 000 Informatik, Wissen, Systeme |
500 Naturwissenschaften und Mathematik / 510 Mathematik | |
BKL-Klassifikation: | 31 Mathematik / 31.80 Angewandte Mathematik |
56 Bauwesen / 56.03 Methoden im Bauingenieurwesen | |
Sammlungen: | Bauhaus-Universität Weimar / Internationales Kolloquium über Anwendungen der Informatik und Mathematik in Architektur und Bauwesen, IKM, Weimar / Internationales Kolloquium über Anwendungen der Informatik und Mathematik in Architektur und Bauwesen, IKM, Weimar, 20. 2015 |
Lizenz (Deutsch): | Creative Commons 4.0 - Namensnennung-Nicht kommerziell (CC BY-NC 4.0) |