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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 F...

https://devfeature-collection.sl.nsw.gov.au/record/TN_cdi_gale_infotracmisc_A700924896

Rolling Bearing Fault Feature Extraction Using Local Maximum Synchrosqueezing Transform and Global Fuzzy Entropy

About this item

Full title

Rolling Bearing Fault Feature Extraction Using Local Maximum Synchrosqueezing Transform and Global Fuzzy Entropy

Publisher

International Institute of Acoustics and Vibration

Journal title

International journal of acoustics and vibration, 2022-03, Vol.27 (1), p.37

Language

English

Formats

Publication information

Publisher

International Institute of Acoustics and Vibration

Subjects

More information

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...

Alternative Titles

Full title

Rolling Bearing Fault Feature Extraction Using Local Maximum Synchrosqueezing Transform and Global Fuzzy Entropy

Authors, Artists and Contributors

Identifiers

Primary Identifiers

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

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