Affiliation:
1. Institute of Digital and Autonomous Construction, Hamburg University of Technology, Blohmstraße 15, 21079 Hamburg, Germany
Abstract
Structural health monitoring (SHM) is a non-destructive testing method that supports the condition assessment and lifetime estimation of civil infrastructure. Sensor faults may result in the loss of valuable data and erroneous structural condition assessments and lifetime estimations, in the worst case with structural damage remaining undetected. As a result, the concepts of fault diagnosis (FD) have been increasingly adopted by the SHM community. However, most FD concepts for SHM consider only single-fault occurrence, which may oversimplify actual fault occurrences in real-world SHM systems. This paper presents an adaptive FD approach for SHM systems that addresses simultaneous faults occurring in multiple sensors. The adaptive FD approach encompasses fault detection, isolation, and accommodation, and it builds upon analytical redundancy, which uses correlated data from multiple sensors of an SHM system. Specifically, faults are detected using the predictive capabilities of artificial neural network (ANN) models that leverage correlations within sensor data. Upon defining time instances of fault occurrences in the sensor data, faults are isolated by analyzing the moving average of individual sensor data around the time instances. For fault accommodation, the ANN models are adapted by removing faulty sensors and by using sensor data prior to the occurrence of faults to produce virtual outputs that substitute the faulty sensor data. The proposed adaptive FD approach is validated via two tests using sensor data recorded by an SHM system installed on a railway bridge. The results demonstrate that the proposed approach is capable of ensuring the accuracy, reliability, and performance of real-world SHM systems, in which faults in multiple sensors occur simultaneously.
Funder
German Research Foundation
German Federal Ministry for Digital and Transport
German Federal Ministry of Education and Research
Subject
Computer Science Applications,Geotechnical Engineering and Engineering Geology,General Materials Science,Building and Construction,Civil and Structural Engineering
Reference33 articles.
1. Sensor data management technologies for infrastructure asset management;Wang;Sensor Technologies for Civil Infrastructures,2014
2. Structural Health Monitoring: State of the Art and Perspectives;Liu;JOM,2012
3. Considerations over the Italian road bridge infrastructure safety after the Polcevera viaduct collapse: Past errors and future perspectives;Bazzucchi;Frat. Integrita Strutt.,2018
4. Smarsly, K., Theiler, M., and Dragos, K. (2017, January 10). IFC-based modeling of cyber-physical systems in civil engineering. Proceedings of the 24th EG-ICE International Workshop on Intelligent Computing in Engineering, Nottingham, UK.
5. Theiler, M., Dragos, K., and Smarsly, K. (2017, January 12). BIM-based design of structural health monitoring systems. Proceedings of the 11th International Workshop on Structural Health Monitoring, Stanford, CA, USA.
Cited by
7 articles.
订阅此论文施引文献
订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献