SVM-based Bearing Anomaly Identification with Self-tuning Network-Fuzzy Robust Proportional Multi Integral and Smart Autoregressive model
- Alternative Title
- SVM-based Bearing Anomaly Identification with Self-tuning Network-Fuzzy Robust Proportional Multi Integral and Smart Autoregressive model
- Abstract
- SVM-based Bearing Anomaly Identification with Self-tuning Network-Fuzzy Robust Proportional Multi Integral and Smart Autoregressive model
https://doi.org/10.3390/app11062784
- Issued Date
- 2021
- Type
- Article
- Keyword
- autoregressive with external uncertainties; indirect self-tuning observer; machine learning–based classification technique; machine learning–based regression; rolling element bearing; rotating machine
- URI
- https://oak.ulsan.ac.kr/handle/2021.oak/9057
https://ulsan-primo.hosted.exlibrisgroup.com/primo-explore/fulldisplay?docid=TN_cdi_doaj_primary_oai_doaj_org_article_74bf8b1bd86843c9957150badef32369&context=PC&vid=ULSAN&lang=ko_KR&search_scope=default_scope&adaptor=primo_central_multiple_fe&tab=default_tab&query=any,contains,SVM-based%20Bearing%20Anomaly%20Identification%20with%20Self-tuning%20Network-Fuzzy%20Robust%20Proportional%20Multi%20Integral%20and%20Smart%20Autoregressive%20model&offset=0&pcAvailability=true
- Publisher
- APPLIED SCIENCES-BASEL
- Location
- 스위스
- Language
- 영어
- ISSN
- 2076-3417
- Citation Volume
- 11
- Citation Number
- 6
- Citation Start Page
- 2784
- Citation End Page
- 2784
- Appears in Collections:
- Engineering > IT Convergence
- 파일 목록
-
-
관련 파일이 존재하지 않습니다.
-
Items in Repository are protected by copyright, with all rights reserved, unless otherwise indicated.