Combining transfer learning and numerical modelling to deal with the lack of training data in data-based SHM
dc.contributor.author | Battu, RS | |
dc.contributor.author | Agathos, K | |
dc.contributor.author | Monsalve, JML | |
dc.contributor.author | Worden, K | |
dc.contributor.author | Papatheou, E | |
dc.date.accessioned | 2024-09-13T11:41:11Z | |
dc.date.issued | 2024-09-01 | |
dc.date.updated | 2024-09-13T10:50:46Z | |
dc.description.abstract | Structural health monitoring (SHM) involves continuously surveilling the performance of structures to identify progressive damage or deterioration that might evolve over time. Recently, machine learning (ML) algorithms have been successfully employed in various SHM applications, including damage detection. However, supervised ML algorithms often require labelled data for multiple possible damage states of the structure for successful damage identification. Although it may be feasible to gather such data for low-value structures, obtaining damage data for expensive structures such as aircraft could be highly challenging. Herein, this data insufficiency is addressed by combining Finite Element (FE) models with domain adaptation, specifically transfer component analysis (TCA) and joint domain adaptation (JDA). The proposed methodology is showcased in two case studies, a Brake–Reuß beam, where damage scenarios correspond to different torque settings on a lap joint and a wingbox laboratory structure where damage is introduced as saw-cuts. Supervised learning algorithms in the form of Artificial Neural Networks (ANNs) and K-Nearest Neighbours (KNNs) are trained based on FE data after domain adaptation is applied and are then tested with the experimental data. It is shown that even though the performance of classifiers in distinct scenarios of dual, three, four and five-class cases is sensitive to choices in the training stage, the use of TCA or JDA allows for the use of FE data for training and significantly reduces the need for expensive experimental damage data to be used for training. These results can pave the way for a broader use of ML algorithms in SHM of critical and/or expensive structures. | en_GB |
dc.description.sponsorship | Engineering and Physical Sciences Research Council (EPSRC) | en_GB |
dc.identifier.citation | Vol. 595, article 118710 | en_GB |
dc.identifier.doi | https://doi.org/10.1016/j.jsv.2024.118710 | |
dc.identifier.grantnumber | EP/W005816/1 | en_GB |
dc.identifier.uri | http://hdl.handle.net/10871/137430 | |
dc.identifier | ORCID: 0000-0001-6024-6908 (Battu, RS) | |
dc.identifier | ORCID: 0000-0002-9556-417X (Agathos, K) | |
dc.identifier | ORCID: 0000-0001-5501-8839 (Monsalve, JM Londono) | |
dc.identifier | ORCID: 0000-0003-1927-1348 (Papatheou, E) | |
dc.language.iso | en | en_GB |
dc.publisher | Elsevier | en_GB |
dc.rights | © 2024 The Author(s). Published by Elsevier Ltd. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/). | en_GB |
dc.subject | Structural health monitoring | en_GB |
dc.subject | Machine learning | en_GB |
dc.subject | Damage detection | en_GB |
dc.subject | Artificial neural | en_GB |
dc.subject | Networks | en_GB |
dc.subject | Domain adaptation | en_GB |
dc.title | Combining transfer learning and numerical modelling to deal with the lack of training data in data-based SHM | en_GB |
dc.type | Article | en_GB |
dc.date.available | 2024-09-13T11:41:11Z | |
dc.identifier.issn | 0022-460X | |
exeter.article-number | 118710 | |
dc.description | This is the final version. Available on open access from Elsevier via the DOI in this record. | en_GB |
dc.identifier.eissn | 1095-8568 | |
dc.identifier.journal | Journal of Sound and Vibration | en_GB |
dc.rights.uri | https://creativecommons.org/licenses/by/4.0/ | en_GB |
dcterms.dateAccepted | 2024-08-29 | |
rioxxterms.version | VoR | en_GB |
rioxxterms.licenseref.startdate | 2024-09-01 | |
rioxxterms.type | Journal Article/Review | en_GB |
refterms.dateFCD | 2024-09-13T11:35:53Z | |
refterms.versionFCD | VoR | |
refterms.dateFOA | 2024-09-13T11:41:12Z | |
refterms.panel | B | en_GB |
refterms.dateFirstOnline | 2024-09-01 |
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