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Bearing Anomaly Recognition Using an Intelligent Digital Twin Integrated with Machine Learning

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Abstract
In this study, the application of an intelligent digital twin integrated with machine learning
for bearing anomaly detection and crack size identification will be observed. The intelligent digital
twin has two main sections: signal approximation and intelligent signal estimation. The mathematical
vibration bearing signal approximation is integrated with machine learning-based signal
approximation to approximate the bearing vibration signal in normal conditions. After that, the
combination of the Kalman filter, high-order variable structure technique, and adaptive neural-fuzzy
technique is integrated with the proposed signal approximation technique to design an intelligent
digital twin. Next, the residual signals will be generated using the proposed intelligent digital twin
and the original RAWsignals. The machine learning approach will be integrated with the proposed
intelligent digital twin for the classification of the bearing anomaly and crack sizes. The CaseWestern
Reserve University bearing dataset is used to test the impact of the proposed scheme. Regarding the
experimental results, the average accuracy for the bearing fault pattern recognition and crack size
identification will be, respectively, 99.5% and 99.6%.
Author(s)
필탄 파르진김종면
Issued Date
2021
Type
Article
Keyword
adaptive neural-fuzzy approachbearing anomaly detectiondigital twinhigh-order variable structure techniqueKalman filtersupport vector algorithm
DOI
10.3390/app11104602
URI
https://oak.ulsan.ac.kr/handle/2021.oak/9066
https://ulsan-primo.hosted.exlibrisgroup.com/primo-explore/fulldisplay?docid=TN_cdi_doaj_primary_oai_doaj_org_article_f4ca276b981c4690948a5c7cfc207348&context=PC&vid=ULSAN&lang=ko_KR&search_scope=default_scope&adaptor=primo_central_multiple_fe&tab=default_tab&query=any,contains,Bearing%20Anomaly%20Recognition%20Using%20an%20Intelligent%20Digital%20Twin%20Integrated%20with%20Machine%20Learning&offset=0&pcAvailability=true
Publisher
APPLIED SCIENCES-BASEL
Location
스위스
Language
영어
ISSN
2076-3417
Citation Volume
11
Citation Number
10
Citation Start Page
4602
Citation End Page
4602
Appears in Collections:
Engineering > IT Convergence
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