UM  > Faculty of Science and Technology
Residential Collegefalse
Sensor fault detection, localization, and reconstruction for online structural identification
Huang, Ke1; Yuen, Ka Veng2,3; Wang, Lei1; Jiang, Tianyong1; Dai, Lizhao1
Source PublicationStructural Control and Health Monitoring

In this study, a novel sensor fault detection, localization, and reconstruction approach is proposed for online structural identification. The proposed method avoids the requirement of massive training data from the normal operating sensor network and presents a computationally efficient approach to diagnose and estimate the typical sensor faults in a dense sensor network for time-varying structural systems. First, a two-level Bayesian model class selection strategy is introduced for sensor fault detection and localization. By evaluating the plausibilities of the model classes in the two-level strategy, detection and localization of possible faulty sensors can be realized with low computational cost. After detecting and locating the faulty sensors, an online updating algorithm based on a Kalman filter and an extended Kalman filter is then utilized to simultaneously estimate the sensor faults and identify the structural system. Two illustrative examples are presented to validate the efficacy of the proposed method. The results show that the proposed approach offers a reliable and efficient sensor validation methodology for online structural identification.

KeywordBayesian Inference Faulty Sensor Kalman Filter Online Estimation Sensor Validation Structural Health Monitoring
URLView the original
Indexed BySCIE
WOS Research AreaConstruction & Building Technology ; Engineering ; Instruments & Instrumentation
WOS SubjectConstruction & Building Technology ; Engineering, Civil ; Instruments & Instrumentation
WOS IDWOS:000742657700001
Scopus ID2-s2.0-85122733211
Fulltext Access
Citation statistics
Cited Times [WOS]:0   [WOS Record]     [Related Records in WOS]
Document TypeJournal article
CollectionFaculty of Science and Technology
Corresponding AuthorWang, Lei
Affiliation1.School of Civil Engineering, Changsha University of Science and Technology, Changsha, China
2.State Key Laboratory of Internet of Things for Smart City and Department of Civil and Environmental Engineering, University of Macau, Macao
3.Guangdong-Hong Kong-Macao Joint Laboratory for Smart Cities, University of Macau, Macao
Recommended Citation
GB/T 7714
Huang, Ke,Yuen, Ka Veng,Wang, Lei,et al. Sensor fault detection, localization, and reconstruction for online structural identification[J]. Structural Control and Health Monitoring,2022,29(4).
APA Huang, Ke,Yuen, Ka Veng,Wang, Lei,Jiang, Tianyong,&Dai, Lizhao.(2022).Sensor fault detection, localization, and reconstruction for online structural identification.Structural Control and Health Monitoring,29(4).
MLA Huang, Ke,et al."Sensor fault detection, localization, and reconstruction for online structural identification".Structural Control and Health Monitoring 29.4(2022).
Files in This Item:
There are no files associated with this item.
Related Services
Recommend this item
Usage statistics
Export to Endnote
Google Scholar
Similar articles in Google Scholar
[Huang, Ke]'s Articles
[Yuen, Ka Veng]'s Articles
[Wang, Lei]'s Articles
Baidu academic
Similar articles in Baidu academic
[Huang, Ke]'s Articles
[Yuen, Ka Veng]'s Articles
[Wang, Lei]'s Articles
Bing Scholar
Similar articles in Bing Scholar
[Huang, Ke]'s Articles
[Yuen, Ka Veng]'s Articles
[Wang, Lei]'s Articles
Terms of Use
No data!
Social Bookmark/Share
All comments (0)
No comment.

Items in the repository are protected by copyright, with all rights reserved, unless otherwise indicated.