Rogers, T.J. orcid.org/0000-0002-3433-3247, Worden, K. and Cross, E.J. (2020) On the application of Gaussian process latent force models for joint input-state-parameter estimation : With a view to Bayesian operational identification. Mechanical Systems and Signal Processing, 140. 106580. ISSN 0888-3270
Abstract
The problem of identifying dynamic structural systems is of key interest to modern engineering practice and is often a first step in an analysis chain, such as validation of computer models or structural health monitoring. While this topic has been well covered for tests conducted in a laboratory setting, identification of full-scale structures in place remains challenging. Additionally, during in service assessment, it is often not possible to measure the loading that a given structure is subjected to; this could be due to practical limitations or cost. Current solutions to this problem revolve around assumptions regarding the nature of the load a structure is subject to; almost exclusively this is assumed to be a white Gaussian noise. However, in many cases this assumption is insufficient and can lead to biased results in system identification. This current work presents a model which attempts the system identification task (in terms of the parametric estimation) in conjunction with estimation of the inputs to the system and the latent states --- the displacements and velocities of the system. Within this paper, a Bayesian framework is presented for rigorous uncertainty quantification over both the system parameters and the unknown input signal. A Gaussian process latent force model allows a flexible Bayesian prior to be placed over the unknown forcing signal, which in conjunction with the state-space representation, allows fully Bayesian inference over the complete dynamic system and the unknown inputs.
Metadata
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Copyright, Publisher and Additional Information: | © 2020 Elsevier Ltd. This is an author produced version of a paper subsequently published in Mechanical Systems and Signal Processing. Uploaded in accordance with the publisher's self-archiving policy. Article available under the terms of the CC-BY-NC-ND licence (https://creativecommons.org/licenses/by-nc-nd/4.0/). | ||||
Keywords: | Bayesian; System Identification; Operational Modal Analysis; Gaussian Process; Latent Force Model | ||||
Dates: |
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Institution: | The University of Sheffield | ||||
Academic Units: | The University of Sheffield > Faculty of Engineering (Sheffield) > Department of Mechanical Engineering (Sheffield) | ||||
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Depositing User: | Symplectic Sheffield | ||||
Date Deposited: | 18 Feb 2020 10:48 | ||||
Last Modified: | 28 Feb 2021 01:38 | ||||
Status: | Published | ||||
Publisher: | Elsevier | ||||
Refereed: | Yes | ||||
Identification Number: | https://doi.org/10.1016/j.ymssp.2019.106580 |