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Machine Learning-Based Robust Feedback Observer for Fault Diagnosis in Bearing

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Abstract
Rolling element bearing (REB) represent a class of nonlinear and
multiple-degrees-of-freedom rotating machines that have pronounced coupling
effects and can be used in various industries. The challenge of understanding
complexity in a bearing’s dynamic behavior, coupling effects, and sources of
uncertainty presents substantial challenges regarding fault diagnosis (FD) in a
REB. Thus, a proposed FD algorithm, based on an TSK fuzzy multi structure
feedback observer, is represented. Due to the effect of the system’s complexities
and uncertainties for FD, a feedback observer (FO) is proposed. To address the
FO drawbacks for FD in the REB such as robustness, the multi structure
technique is represented. In addition, the TSK fuzzy algorithm is applied to the
multi structure FO (MSFO) to increase the performance of signal estimation and
reliability. In addition, the energy residual signals are generated and the machine
learning technique known as a support vector machine (SVM) adaptively
derives the threshold values that are used for classification the faults. The
effectiveness of the proposed technique is validated using a Case Western
Reverse University (CWRU) vibration dataset.
Author(s)
필탄 파르진김종면
Issued Date
2021
Type
Article
Keyword
Roller bearing elementFault diagnosisFeedback observerVariable structure techniqueTSK fuzzy algorithmSupport vector machine
DOI
10.1007/978-3-030-51156-2_129
URI
https://oak.ulsan.ac.kr/handle/2021.oak/9030
https://ulsan-primo.hosted.exlibrisgroup.com/primo-explore/fulldisplay?docid=TN_cdi_springer_books_10_1007_978_3_030_51156_2_129&context=PC&vid=ULSAN&lang=ko_KR&search_scope=default_scope&adaptor=primo_central_multiple_fe&tab=default_tab&query=any,contains,Machine%20Learning-Based%20Robust%20Feedback%20Observer%20for%20Fault%20Diagnosis%20in%20Bearing&sortby=rank&pcAvailability=true
Publisher
Advances in Intelligent Systems and Computing
Location
스위스
Language
영어
Citation Volume
1197
Citation Number
1
Citation Start Page
1107
Citation End Page
1115
Appears in Collections:
Engineering > IT Convergence
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