Damage Detection and Isolation from Limited Experimental Data Using Simple Simulations and Knowledge Transfer
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Khan, Asif | - |
dc.contributor.author | Kim, Jun-Sik | - |
dc.contributor.author | Kim, Heung Soo | - |
dc.date.accessioned | 2022-03-28T02:40:00Z | - |
dc.date.available | 2022-03-28T02:40:00Z | - |
dc.date.created | 2022-03-28 | - |
dc.date.issued | 2022-01 | - |
dc.identifier.issn | 2227-7390 | - |
dc.identifier.uri | https://scholarworks.bwise.kr/kumoh/handle/2020.sw.kumoh/20808 | - |
dc.description.abstract | A simulation model can provide insight into the characteristic behaviors of different health states of an actual system; however, such a simulation cannot account for all complexities in the system. This work proposes a transfer learning strategy that employs simple computer simulations for fault diagnosis in an actual system. A simple shaft-disk system was used to generate a substantial set of source data for three health states of a rotor system, and that data was used to train, validate, and test a customized deep neural network. The deep learning model, pretrained on simulation data, was used as a domain and class invariant generalized feature extractor, and the extracted features were processed with traditional machine learning algorithms. The experimental data sets of an RK4 rotor kit and a machinery fault simulator (MFS) were employed to assess the effectiveness of the proposed approach. The proposed method was also validated by comparing its performance with the pre-existing deep learning models of GoogleNet, VGG16, ResNet18, AlexNet, and SqueezeNet in terms of feature extraction, generalizability, computational cost, and size and parameters of the networks. | - |
dc.language | 영어 | - |
dc.language.iso | en | - |
dc.publisher | MDPI | - |
dc.title | Damage Detection and Isolation from Limited Experimental Data Using Simple Simulations and Knowledge Transfer | - |
dc.type | Article | - |
dc.contributor.affiliatedAuthor | Kim, Jun-Sik | - |
dc.identifier.doi | 10.3390/math10010080 | - |
dc.identifier.wosid | 000751182500001 | - |
dc.identifier.bibliographicCitation | MATHEMATICS, v.10, no.1 | - |
dc.relation.isPartOf | MATHEMATICS | - |
dc.citation.title | MATHEMATICS | - |
dc.citation.volume | 10 | - |
dc.citation.number | 1 | - |
dc.type.rims | ART | - |
dc.type.docType | Article | - |
dc.description.journalClass | 1 | - |
dc.description.isOpenAccess | Y | - |
dc.description.journalRegisteredClass | scie | - |
dc.description.journalRegisteredClass | scopus | - |
dc.relation.journalResearchArea | Mathematics | - |
dc.relation.journalWebOfScienceCategory | Mathematics | - |
dc.subject.keywordPlus | FAULT-DIAGNOSIS | - |
dc.subject.keywordPlus | ROTOR SYSTEM | - |
dc.subject.keywordPlus | WAVELET ANALYSIS | - |
dc.subject.keywordPlus | OIL WHIRL | - |
dc.subject.keywordPlus | MODEL | - |
dc.subject.keywordPlus | MISALIGNMENT | - |
dc.subject.keywordPlus | UNBALANCE | - |
dc.subject.keywordPlus | STABILITY | - |
dc.subject.keywordPlus | VIBRATION | - |
dc.subject.keywordPlus | CRACK | - |
dc.subject.keywordAuthor | computer simulations | - |
dc.subject.keywordAuthor | actual systems | - |
dc.subject.keywordAuthor | deep learning | - |
dc.subject.keywordAuthor | transfer learning | - |
dc.subject.keywordAuthor | autonomous feature extraction | - |
dc.subject.keywordAuthor | machine learning | - |
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