Tracking time-varying properties using quasi time-invariant models with Bayesian dynamic programming

Tracking the temporal variation of the properties of a system is relevant in different settings when data of extended duration is available, e.g., anomaly detection, condition monitoring, and trend identification. One simple approach is to divide the data into non-overlapping segments and then ident...

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Main Authors: Yang, Yanping, Zhu, Zuo, Au, Siu-Kui
Other Authors: School of Civil and Environmental Engineering
Format: Article
Language:English
Published: 2024
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Online Access:https://hdl.handle.net/10356/180079
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Institution: Nanyang Technological University
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spelling sg-ntu-dr.10356-1800792024-09-20T15:33:29Z Tracking time-varying properties using quasi time-invariant models with Bayesian dynamic programming Yang, Yanping Zhu, Zuo Au, Siu-Kui School of Civil and Environmental Engineering University of Exeter, UK Engineering Quasi time-invariant model QTI model Model property tracking Bayesian dynamic programming BAYOMA Structural health monitoring Tracking the temporal variation of the properties of a system is relevant in different settings when data of extended duration is available, e.g., anomaly detection, condition monitoring, and trend identification. One simple approach is to divide the data into non-overlapping segments and then identify the model properties of each segment individually using a time-invariant model within the segment. The potential change of the system is then investigated by tracking the variations of model properties from one segment to another. In this context, a dynamic programming approach has been developed recently that determines the ‘best partitioning’ of data segments, between which a change of model parameters takes place in the sense of Bayesian model selection. As a change can result when any one of the parameters has changed in a statistically significant manner, a basic question is concerned with what constitutes a suitable model that meets specific monitoring objectives. E.g., should all or only some of the parameters be allowed to change? Motivated by this, a quasi time-invariant (QTI) modeling methodology is proposed in this work where only some (rather than all) parameters are allowed to change across data segments. Computational issues associated with this new class of models are addressed, e.g., the efficient calculation of posterior most probable value and covariance matrix, and Bayesian evidence in the context of dynamic programming. Focusing on modal property (natural frequencies, damping ratios, etc.) tracking with ambient data, the proposed methodology with QTI model is investigated with synthetic and laboratory data; and applied to field data of a tall building during a typhoon event. The results from field data are compared with those from existing methods. Ministry of Education (MOE) Nanyang Technological University Submitted/Accepted version The research presented in this paper is supported by Academic Research Fund Tier 1 (RG68/22) from the Ministry of Education, Singapore. The first author would like to acknowledge the graduate research scholarship offered by NTU. The second author gratefully acknowledges the support of the UK Engineering and Physical Sciences Research Council (EPSRC) through the ROSEHIPS project (Grant EP/W005816/1). 2024-09-18T06:28:17Z 2024-09-18T06:28:17Z 2025 Journal Article Yang, Y., Zhu, Z. & Au, S. (2025). Tracking time-varying properties using quasi time-invariant models with Bayesian dynamic programming. Mechanical Systems and Signal Processing, 223, 111546-. https://dx.doi.org/10.1016/j.ymssp.2024.111546 0888-3270 https://hdl.handle.net/10356/180079 10.1016/j.ymssp.2024.111546 223 111546 en RG68/22 EP/W005816/1 Mechanical Systems and Signal Processing 10.7910/DVN/CTNLFT © 2024 Elsevier Ltd. All rights reserved. This article may be downloaded for personal use only. Any other use requires prior permission of the copyright holder. The Version of Record is available online at http://doi.org/10.1016/j.ymssp.2024.111546. application/pdf
institution Nanyang Technological University
building NTU Library
continent Asia
country Singapore
Singapore
content_provider NTU Library
collection DR-NTU
language English
topic Engineering
Quasi time-invariant model
QTI model
Model property tracking
Bayesian dynamic programming
BAYOMA
Structural health monitoring
spellingShingle Engineering
Quasi time-invariant model
QTI model
Model property tracking
Bayesian dynamic programming
BAYOMA
Structural health monitoring
Yang, Yanping
Zhu, Zuo
Au, Siu-Kui
Tracking time-varying properties using quasi time-invariant models with Bayesian dynamic programming
description Tracking the temporal variation of the properties of a system is relevant in different settings when data of extended duration is available, e.g., anomaly detection, condition monitoring, and trend identification. One simple approach is to divide the data into non-overlapping segments and then identify the model properties of each segment individually using a time-invariant model within the segment. The potential change of the system is then investigated by tracking the variations of model properties from one segment to another. In this context, a dynamic programming approach has been developed recently that determines the ‘best partitioning’ of data segments, between which a change of model parameters takes place in the sense of Bayesian model selection. As a change can result when any one of the parameters has changed in a statistically significant manner, a basic question is concerned with what constitutes a suitable model that meets specific monitoring objectives. E.g., should all or only some of the parameters be allowed to change? Motivated by this, a quasi time-invariant (QTI) modeling methodology is proposed in this work where only some (rather than all) parameters are allowed to change across data segments. Computational issues associated with this new class of models are addressed, e.g., the efficient calculation of posterior most probable value and covariance matrix, and Bayesian evidence in the context of dynamic programming. Focusing on modal property (natural frequencies, damping ratios, etc.) tracking with ambient data, the proposed methodology with QTI model is investigated with synthetic and laboratory data; and applied to field data of a tall building during a typhoon event. The results from field data are compared with those from existing methods.
author2 School of Civil and Environmental Engineering
author_facet School of Civil and Environmental Engineering
Yang, Yanping
Zhu, Zuo
Au, Siu-Kui
format Article
author Yang, Yanping
Zhu, Zuo
Au, Siu-Kui
author_sort Yang, Yanping
title Tracking time-varying properties using quasi time-invariant models with Bayesian dynamic programming
title_short Tracking time-varying properties using quasi time-invariant models with Bayesian dynamic programming
title_full Tracking time-varying properties using quasi time-invariant models with Bayesian dynamic programming
title_fullStr Tracking time-varying properties using quasi time-invariant models with Bayesian dynamic programming
title_full_unstemmed Tracking time-varying properties using quasi time-invariant models with Bayesian dynamic programming
title_sort tracking time-varying properties using quasi time-invariant models with bayesian dynamic programming
publishDate 2024
url https://hdl.handle.net/10356/180079
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