An audio-based framework for anomaly detection in large-scale structural testing

Munko, M.J. orcid.org/0009-0009-2040-5025, Cuthill, F. orcid.org/0000-0002-8674-1656, Camacho, M.A.V. et al. (2 more authors) (2025) An audio-based framework for anomaly detection in large-scale structural testing. Engineering Applications of Artificial Intelligence, 142. 109889. ISSN 0952-1976

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Item Type: Article
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© 2024 The Authors. This is an Open Access article distributed under the terms of the Creative Commons Attribution Licence (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Keywords: Anomaly detection; Fatigue testing; Artificial neural network; Convolutional autoencoder; Wavelet scattering
Dates:
  • Published: 15 February 2025
  • Published (online): 30 December 2024
  • Accepted: 14 December 2024
Institution: The University of Sheffield
Academic Units: The University of Sheffield > Faculty of Engineering (Sheffield)
Depositing User: Symplectic Sheffield
Date Deposited: 02 Jan 2025 11:38
Last Modified: 02 Jan 2025 11:38
Published Version: https://doi.org/10.1016/j.engappai.2024.109889
Status: Published
Publisher: Elsevier BV
Refereed: Yes
Identification Number: 10.1016/j.engappai.2024.109889
Open Archives Initiative ID (OAI ID):

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