A Kullback–Leibler divergence method for input–system–state identification
A Kullback–Leibler divergence method for input–system–state identification
The capability of a novel Kullback–Leibler divergence method is examined herein within the Kalman filter framework to select the input–parameter–state estimation execution with the most plausible results. This identification suffers from the uncertainty related to obtaining different results from different initial parameter set guesses, and the examined approach uses the information gained from the data in going from the prior to the posterior distribution to address the issue. Firstly, the Kalman filter is performed for a number of different initial parameter sets providing the system input–parameter–state estimation. Secondly, the resulting posteriordistributions are compared simultaneously to the initial prior distributions using the Kullback–Leibler divergence. Finally, the identification with the least Kullback–Leibler divergence is selected as the one with the most plausible results. Importantly, the method is shown to select the better performed identification in linear, nonlinear, and limited information applications, providing a powerful tool for system monitoring.
Kullback–Leibler divergence, Limited information/sensing structural health monitoring and damage-fault detection, Output-only input-parameter-state nonlinear estimation, Residual-based Kalman filter, Unknown/unmeasured load-system identification, Unscented Kalman filter
Impraimakis, Marios
e8a4540d-2348-4422-9d13-d335b128b02b
20 January 2024
Impraimakis, Marios
e8a4540d-2348-4422-9d13-d335b128b02b
Impraimakis, Marios
(2024)
A Kullback–Leibler divergence method for input–system–state identification.
Journal of Sound and Vibration, 569, [117965].
(doi:10.1016/j.jsv.2023.117965).
Abstract
The capability of a novel Kullback–Leibler divergence method is examined herein within the Kalman filter framework to select the input–parameter–state estimation execution with the most plausible results. This identification suffers from the uncertainty related to obtaining different results from different initial parameter set guesses, and the examined approach uses the information gained from the data in going from the prior to the posterior distribution to address the issue. Firstly, the Kalman filter is performed for a number of different initial parameter sets providing the system input–parameter–state estimation. Secondly, the resulting posteriordistributions are compared simultaneously to the initial prior distributions using the Kullback–Leibler divergence. Finally, the identification with the least Kullback–Leibler divergence is selected as the one with the most plausible results. Importantly, the method is shown to select the better performed identification in linear, nonlinear, and limited information applications, providing a powerful tool for system monitoring.
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Accepted/In Press date: 26 July 2023
e-pub ahead of print date: 29 July 2023
Published date: 20 January 2024
Additional Information:
Funding Information:
The author would like to gratefully acknowledge the reviewers for their constructive comments, and Andrew W. Smyth for the previous insightful discussions on the topic.
Publisher Copyright:
© 2023 The Author(s)
Keywords:
Kullback–Leibler divergence, Limited information/sensing structural health monitoring and damage-fault detection, Output-only input-parameter-state nonlinear estimation, Residual-based Kalman filter, Unknown/unmeasured load-system identification, Unscented Kalman filter
Identifiers
Local EPrints ID: 481093
URI: http://eprints.soton.ac.uk/id/eprint/481093
ISSN: 0022-460X
PURE UUID: 7526527b-5885-4be9-a0a7-efeb7c3cceda
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Date deposited: 15 Aug 2023 16:47
Last modified: 17 Mar 2024 04:05
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Author:
Marios Impraimakis
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