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Improving Bearing Diagnostic Performance by Using New Discriminatory Fault-Feature Evaluation

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Abstract
Locating different defect types in bearings using the information of the
characteristic frequencies in the envelope power spectrum of analyzed acoustic emission
(AE) signals has been widely utilized. However, this approach only shows
effectiveness as the rotational speed of bearing elements is constant. In contrast,
if the bearing speed frequently alters during operation, the value of these characteristic
frequencies is not stable, therefore, it is useless for diagnostic purposes. In
order to resolve this issue, this study proposes an approach that (a) adopts heterogeneous
feature modes to extract as many statistical features as possible in transformed
domains (i.e., the time domain, the frequency domain, and the wavelet domain); (b)
explores the most discriminatory features using new feature selection scheme. The
scheme is the combination of the genetic algorithm (GA)-based feature analysis and
the k-nearest neighbors (k-NN); (c) the defect types of a typical bearing are categorized
by the k-NN-based classifier. The performance of the proposed method is
validated by two datasets of AE samples measured from our bearing testbed.
Author(s)
짜 윁김종면
Issued Date
2021
Type
Article
DOI
10.1007/978-981-15-9837-1_11
URI
https://oak.ulsan.ac.kr/handle/2021.oak/9054
https://ulsan-primo.hosted.exlibrisgroup.com/primo-explore/fulldisplay?docid=TN_cdi_springer_books_10_1007_978_981_15_9837_1_11&context=PC&vid=ULSAN&lang=ko_KR&search_scope=default_scope&adaptor=primo_central_multiple_fe&tab=default_tab&query=any,contains,Improving%20Bearing%20Diagnostic%20Performance%20by%20Using%20New%20Discriminatory%20Fault-Feature%20Evaluati&offset=0&pcAvailability=true
Publisher
Springer Proceedings in Physics
Location
스위스
Language
영어
Citation Volume
259
Citation Number
1
Citation Start Page
115
Citation End Page
125
Appears in Collections:
Engineering > IT Convergence
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