Bull, L.A., Worden, K., Manson, G. et al. (1 more author) (2018) Active learning for semi-supervised structural health monitoring. Journal of Sound and Vibration. ISSN 0022-460X
Abstract
A critical issue for structural health monitoring (SHM) strategies based on pattern recognition models is a lack of diagnostic labels to explain the measured data. In an engineering context, these descriptive labels are costly to obtain, and as a result, conventional supervised learning is not feasible. Active learning tools look to solve this issue by selecting a limited number of the most informative observations to query for labels. This work presents the application of cluster-adaptive active learning to measured data from aircraft experiments. These tests successfully illustrate the advantages of utilising active learning tools for SHM, and they present the first application/adaptation of active learning methods to engineering data — a MATLAB package is available via GitHub: https://github.com/labull/cluster_based_active_learning.
Metadata
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Copyright, Publisher and Additional Information: | © 2018 Elsevier. 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: | Structural health monitoring; Vibration monitoring; Active learning; Semi-supervised learning; Classification; Pattern recognition | ||||
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: | 11 Sep 2018 11:03 | ||||
Last Modified: | 05 Sep 2019 00:42 | ||||
Published Version: | https://doi.org/10.1016/j.jsv.2018.08.040 | ||||
Status: | Published online | ||||
Publisher: | Elsevier | ||||
Refereed: | Yes | ||||
Identification Number: | https://doi.org/10.1016/j.jsv.2018.08.040 |