Smart Digital Twin-Based Bearing Fault Pattern Recognition
- Abstract
- In this research, the combination of the smart digital twin (SDT) and
the machine learning technique is prescribed to have a reliable fault pattern recognition
in this effort. In the first stage, the SDT for the bearing is designed by
the dynamical system modeling, updated using the data-driven autoregression
approach, and estimate the performance using smart Kalman filter (SKF). Thus,
first, the data-driven-based autoregressive is selected to update the mathematical
model of bearing and design an effective modeling section of the digital twin.
Next, the SKF for the bearing signal estimation is designed by the combination
of the Kalman Filter and fuzzy logic approach. In the second stage, the difference
between original and estimated signals are computed. Finally, in the last stage,
the support vector clustering (SVC) is recommended for clustering the bearing’s
situations. The precision of the proposed procedure for the bearing fault pattern
recognition is around 97.8%.
- Author(s)
- 필탄 파르진; 김종면
- Issued Date
- 2021
- Type
- Article
- Keyword
- Autoregressive; Bearing; Digital twin; Fault analysis; Fuzzy technique; Kalman filter; Support vector clustering
- DOI
- 10.1007/978-3-030-85626-7_1
- URI
- https://oak.ulsan.ac.kr/handle/2021.oak/9080
https://ulsan-primo.hosted.exlibrisgroup.com/primo-explore/fulldisplay?docid=TN_cdi_springer_books_10_1007_978_3_030_85626_7_1&context=PC&vid=ULSAN&lang=ko_KR&search_scope=default_scope&adaptor=primo_central_multiple_fe&tab=default_tab&query=any,contains,Smart%20Digital%20Twin-Based%20Bearing%20Fault%20Pattern%20Recognition&offset=0&pcAvailability=true
- Publisher
- Lecture Notes in Networks and Systems
- Location
- 스위스
- Language
- 영어
- ISSN
- 2367-3370
- Citation Volume
- 307
- Citation Number
- 1
- Citation Start Page
- 3
- Citation End Page
- 10
-
Appears in Collections:
- Engineering > IT Convergence
- 공개 및 라이선스
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