Jones, M.R., Rogers, T.J. orcid.org/0000-0002-3433-3247, Worden, K. orcid.org/0000-0002-1035-238X et al. (1 more author) (2022) A Bayesian methodology for localising acoustic emission sources in complex structures. Mechanical Systems and Signal Processing, 163. 108143. ISSN 0888-3270
Abstract
In the field of structural health monitoring (SHM), the acquisition of acoustic emissions to localise damage sources has emerged as a popular approach. Despite recent advances, the task of locating damage within composite materials and structures that contain non-trivial geometrical features, still poses a significant challenge. Within this paper, a Bayesian source localisation strategy that is robust to these complexities is presented. Under this new framework, a Gaussian process is first used to learn the relationship between source locations and the corresponding difference-in-time-of-arrival values for a number of sensor pairings. As an acoustic emission event with an unknown origin is observed, a mapping is then generated that quantifies the likelihood of the emission location across the surface of the structure. The new probabilistic mapping offers multiple benefits, leading to a localisation strategy that is more informative than deterministic predictions or single-point estimates with an associated confidence bound. The performance of the approach is investigated on a structure with numerous complex geometrical features and demonstrates a favourable performance in comparison to other similar localisation methods.
Metadata
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Copyright, Publisher and Additional Information: | © 2021 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/). | ||||
Keywords: | Bayesian; Acoustic emission; Localisation; Gaussian processes; Complex structure | ||||
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) | ||||
Funding Information: |
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Depositing User: | Symplectic Sheffield | ||||
Date Deposited: | 16 Sep 2021 06:41 | ||||
Last Modified: | 16 Sep 2021 06:41 | ||||
Status: | Published | ||||
Publisher: | Elsevier BV | ||||
Refereed: | Yes | ||||
Identification Number: | https://doi.org/10.1016/j.ymssp.2021.108143 |