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SVM-Based Hybrid Robust PIO Fault Diagnosis for Bearing

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Abstract
Inner, outer, and ball faults are complex non-stationary and nonlinear faults that occurs in rotating machinery such as bearings. Designing an
effective procedure for fault diagnosis (FD) is essential to safe operation of
bearings. To address fault diagnosis issue, a robust, hybrid technique based on
the ARX-Laguerre fuzzy-sliding proportional integral observer (ALFSPIO) for
rolling element bearing (REB) is presented. The main important challenges in the
ARX-Laguerre PI observer are robustness, and estimation accuracy. To address
the robustness issue, sliding observation technique is introduced. Moreover, to
increase the signal estimation accuracy, the fuzzy technique is used in parallel
with ARX-Laguerre sliding PIO. Furthermore, using the ALFSPIO, the residual
energy signals showed more differentiable for fault diagnosis. Beyond the above,
the support vector machine (SVM) is used to fault detection and classification.
The vibration dataset of Case Western Reverse University (CWRU) is used to
validate the effectiveness of the proposed algorithm.
Author(s)
필탄 파르진김종면
Issued Date
2021
Type
Article
Keyword
Roller bearing elementFault diagnosisARX-Laguerre techniquePI observerSliding mode techniqueFuzzy algorithm
DOI
10.1007/978-3-030-51156-2_99
URI
https://oak.ulsan.ac.kr/handle/2021.oak/9031
https://ulsan-primo.hosted.exlibrisgroup.com/primo-explore/fulldisplay?docid=TN_cdi_springer_books_10_1007_978_3_030_51156_2_99&context=PC&vid=ULSAN&lang=ko_KR&search_scope=default_scope&adaptor=primo_central_multiple_fe&tab=default_tab&query=any,contains,SVM-Based%20Hybrid%20Robust%20PIO%20Fault%20Diagnosis%20for%20Bearing&offset=0&pcAvailability=true
Publisher
Advances in Intelligent Systems and Computing
Location
스위스
Language
영어
Citation Volume
1197
Citation Number
1
Citation Start Page
858
Citation End Page
866
Appears in Collections:
Engineering > IT Convergence
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