Bull, L.A. orcid.org/0000-0002-0225-5010, Gardner, P.A. orcid.org/0000-0002-1882-9728, Dervilis, N. orcid.org/0000-0002-5712-7323 et al. (4 more authors) (2021) On the transfer of damage detectors between structures: an experimental case study. Journal of Sound and Vibration, 501. 116072. ISSN 0022-460X
Abstract
Incomplete data – which fail to represent environmental effects or damage – are a significant challenge for structural health monitoring (SHM). Population-based frameworks offer one solution by considering that information might be shared, in some sense, between similar structures. In this work, the data from a group of aircraft tailplanes are considered collectively, in a shared (more consistent) latent space. As a result, the measurements from one tailplane enable damage detection in another, utilising various pair-wise comparisons within the population.
Specifically, Transfer Component Analysis (TCA) is applied to match the normal condition data from different population members. The resulting nonlinear projection leads to a general representation for the normal condition across the population, which informs damage detection via measures of discordancy. The method is applied to a experimental dataset, based on vibration-based laser vibrometer measurements from three tailplanes. By considering the partial datasets together, consistent damage-sensitive features can be defined, leading to an 87% increase in the true positive rate, compared to conventional SHM.
Metadata
Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2021 Elsevier Ltd. This is an author produced version of a paper subsequently published in Journal of Sound and Vibration. 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: | Population-based structural health monitoring; Domain adaptation; Transfer learning; Novelty detection; One-class classification; Damage detection | ||||||||||
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: | 29 Mar 2021 13:44 | ||||||||||
Last Modified: | 07 Mar 2022 01:38 | ||||||||||
Status: | Published | ||||||||||
Publisher: | Elsevier BV | ||||||||||
Refereed: | Yes | ||||||||||
Identification Number: | https://doi.org/10.1016/j.jsv.2021.116072 |