Rolling Bearing Fault Feature Extraction Using Local Maximum Synchrosqueezing Transform and Global F...
Rolling Bearing Fault Feature Extraction Using Local Maximum Synchrosqueezing Transform and Global Fuzzy Entropy
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Publisher
International Institute of Acoustics and Vibration
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Language
English
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Publisher
International Institute of Acoustics and Vibration
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Scope and Contents
Contents
To achieve good performance of fault feature extraction for a rolling bearing, a new feature extraction method is presented in this paper based on local maximum synchrosqueezing transform (LMSST) and global fuzzy entropy (GFuzzyEn). First, targeting the time-varying features of the vibration signals of the rolling bearing, the LMSST algorithm, whic...
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Full title
Rolling Bearing Fault Feature Extraction Using Local Maximum Synchrosqueezing Transform and Global Fuzzy Entropy
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Record Identifier
TN_cdi_gale_infotracmisc_A700924896
Permalink
https://devfeature-collection.sl.nsw.gov.au/record/TN_cdi_gale_infotracmisc_A700924896
Other Identifiers
ISSN
1027-5851
DOI
10.20855/ijav.2022.27.11827
