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Novel Bearing Fault Diagnosis Using Gaussian Mixture Model-Based Fault Band Selection

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Abstract
This paper proposes a Gaussian mixture model-based (GMM) bearing fault band selection (GMM-WBBS) method for signal processing. The proposed method benefits reliable feature extraction using fault frequency oriented Gaussian mixture model (GMM) window series. Selecting exclusively bearing fault frequency harmonics, it eliminates the interference of bearing normal vibrations in the lower frequencies, bearing natural frequencies, and the higher frequency contents that prove to be useful only for anomaly detection but do not provide any insight into the bearing fault location. The features are extracted from time- and frequency- domain signals that exclusively contain the bearing fault frequency harmonics. Classification is done using the Weighted KNN algorithm. The experiments performed with the data containing the vibrations recorded from artificially damaged bearings show the positive effect of utilizing the proposed GMM-WBBS signal processing to filter out the discriminative data of uncertain origin. All comparison methods retrofitted with the proposed method demonstrated classification performance improvements when provided with vibration data with suppressed bearing natural frequencies and higher frequency contents.
Author(s)
말리육 안드레이프로스비린 알렉산데르아흐마드 자후르김철홍김종면
Issued Date
2021
Type
Article
Keyword
bearingelectric motorfault diagnosisfeature extractionfeature selectiongaussian window
DOI
10.3390/s21196579
URI
https://oak.ulsan.ac.kr/handle/2021.oak/9085
https://ulsan-primo.hosted.exlibrisgroup.com/primo-explore/fulldisplay?docid=TN_cdi_doaj_primary_oai_doaj_org_article_1c2d03135494483c9932f3d3f14b1445&context=PC&vid=ULSAN&lang=ko_KR&search_scope=default_scope&adaptor=primo_central_multiple_fe&tab=default_tab&query=any,contains,Novel%20Bearing%20Fault%20Diagnosis%20Using%20Gaussian%20Mixture%20Model-Based%20Fault%20Band%20Selection&offset=0&pcAvailability=true
Publisher
SENSORS
Location
스위스
Language
영어
ISSN
1424-8220
Citation Volume
21
Citation Number
19
Citation Start Page
6579
Citation End Page
6579
Appears in Collections:
Engineering > IT Convergence
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