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Structural anomaly detection based on probabilistic distance measures of transmissibility function and statistical threshold selection scheme
Yan, Wang Ji1,2; Chronopoulos, Dimitrios3,4; Yuen, Ka Veng1,2; Zhu, Yi Chen5
2022-01
Source PublicationMechanical Systems and Signal Processing
ISSN0888-3270
Volume162
Abstract

As a mathematical representation of the output-to-output relationship, transmissibility function (TF) has been extensively applied in structural damage detection due to its robustness to influences of the input variations. As in most engineering fields, dealing with the problem of uncertainty in TF-based feature detection is an issue of fundamental importance. In this study, a new statistical, data-driven damage detection algorithm is proposed by rigorously modelling the variability of TF without postprocessing with circularly-symmetric complex Gaussian ratio distribution. The probabilistic distance of Symmetric Kullback-Leibler (SKL) divergence between TFs under baseline condition and potential damage scenarios which can measure the dissimilarity of probability distributions for the TFs under different states are computed as a damage index (DI) to detect structural anomaly. Compared against Mahalanobis distance which has the implicit assumption that the normal condition set is governed by Gaussian statistics, the probabilistic distance measure proposed in this study can deal with the deviations in TFs not following Gaussian distribution. A statistically rigorous threshold selection scheme integrating Bayesian inference strategy and Monte Carlo discordancy test is proposed to detect the the presence of damage by accommodating the uncertainties of measurements and the probabilistic model of TF. Numerical, experimental, and field test studies are conducted to validate the potential of probabilistic distance measure of TFs in anomaly detection under ambient vibration instead of forced vibration testing. Results demonstrate satisfactory performance of the proposed approach for detecting the existence and quantify the relative damage severity from a global perspective.

KeywordBayesian Analysis Damage Detection Probabilistic Distance Measure Structural Health Monitoring Transmissibility Function Uncertainty Quantification
DOI10.1016/j.ymssp.2021.108009
URLView the original
Indexed BySCIE
Language英語English
WOS Research AreaEngineering
WOS SubjectEngineering, Mechanical
WOS IDWOS:000670296000002
Scopus ID2-s2.0-85110360030
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Cited Times [WOS]:1   [WOS Record]     [Related Records in WOS]
Document TypeJournal article
CollectionUniversity of Macau
Corresponding AuthorYan, Wang Ji
Affiliation1.State Key Laboratory of Internet of Things for Smart City and Department of Civil and Environmental Engineering, University of Macau, China
2.Guangdong–Hong Kong-Macau Joint Laboratory for Smart Cities, China
3.KU Leuven, Department of Mechanical Engineering & Mecha(tro)nic System Dynamics (LMSD), Ghent Technology Campus, 9000, Belgium
4.Institute for Aerospace Technology & The Composites Group, The University of Nottingham, United Kingdom
5.Department of Bridge Engineering, Southeast University, Nanjing, Jiangsu, China
First Author AffilicationUniversity of Macau
Corresponding Author AffilicationUniversity of Macau
Recommended Citation
GB/T 7714
Yan, Wang Ji,Chronopoulos, Dimitrios,Yuen, Ka Veng,et al. Structural anomaly detection based on probabilistic distance measures of transmissibility function and statistical threshold selection scheme[J]. Mechanical Systems and Signal Processing,2022,162.
APA Yan, Wang Ji,Chronopoulos, Dimitrios,Yuen, Ka Veng,&Zhu, Yi Chen.(2022).Structural anomaly detection based on probabilistic distance measures of transmissibility function and statistical threshold selection scheme.Mechanical Systems and Signal Processing,162.
MLA Yan, Wang Ji,et al."Structural anomaly detection based on probabilistic distance measures of transmissibility function and statistical threshold selection scheme".Mechanical Systems and Signal Processing 162(2022).
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