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Online updating and uncertainty quantification using nonstationary output-only measurement
Ka-Veng Yuen; Sin-Chi Kuok
2015
Source PublicationMechanical Systems and Signal Processing
ISSN0888-3270
Volume66-67Pages:62-77
Abstract

Extended Kalman filter (EKF) is widely adopted for state estimation and parametric identification of dynamical systems. In this algorithm, it is required to specify the covariance matrices of the process noise and measurement noise based on prior knowledge. However, improper assignment of these noise covariance matrices leads to unreliable estimation and misleading uncertainty estimation on the system state and model parameters. Furthermore, it may induce diverging estimation. To resolve these problems, we propose a Bayesian probabilistic algorithm for online estimation of the noise parameters which are used to characterize the noise covariance matrices. There are three major appealing features of the proposed approach. First, it resolves the divergence problem in the conventional usage of EKF due to improper choice of the noise covariance matrices. Second, the proposed approach ensures the reliability of the uncertainty quantification. Finally, since the noise parameters are allowed to be time-varying, nonstationary process noise and/or measurement noise are explicitly taken into account. Examples using stationary/nonstationary response of linear/nonlinear time-varying dynamical systems are presented to demonstrate the efficacy of the proposed approach. Furthermore, comparison with the conventional usage of EKF will be provided to reveal the necessity of the proposed approach for reliable model updating and uncertainty quantification.

KeywordBayesian Inference Extended Kalman Filter Noise Covariance Matrices Nonstationary Response Structural Health Monitoring System Identification
DOI10.1016/j.ymssp.2015.05.019
URLView the original
Indexed BySCIE
Language英語English
WOS Research AreaEngineering
WOS SubjectEngineering, Mechanical
WOS IDWOS:000362861700005
PublisherACADEMIC PRESS LTD- ELSEVIER SCIENCE LTD, 24-28 OVAL RD, LONDON NW1 7DX, ENGLAND
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Cited Times [WOS]:36   [WOS Record]     [Related Records in WOS]
Document TypeJournal article
CollectionFaculty of Science and Technology
DEPARTMENT OF CIVIL AND ENVIRONMENTAL ENGINEERING
Corresponding AuthorKa-Veng Yuen
AffiliationFaculty of Science and Technology, University of Macau, Macao, China
First Author AffilicationFaculty of Science and Technology
Corresponding Author AffilicationFaculty of Science and Technology
Recommended Citation
GB/T 7714
Ka-Veng Yuen,Sin-Chi Kuok. Online updating and uncertainty quantification using nonstationary output-only measurement[J]. Mechanical Systems and Signal Processing,2015,66-67:62-77.
APA Ka-Veng Yuen,&Sin-Chi Kuok.(2015).Online updating and uncertainty quantification using nonstationary output-only measurement.Mechanical Systems and Signal Processing,66-67,62-77.
MLA Ka-Veng Yuen,et al."Online updating and uncertainty quantification using nonstationary output-only measurement".Mechanical Systems and Signal Processing 66-67(2015):62-77.
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