Feature extraction based on vibration signal decomposition for fault diagnosis of rolling bearings
Feature extraction based on vibration signal decomposition for fault diagnosis of rolling bearings
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Publisher
London: Springer London
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Language
English
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Publisher
London: Springer London
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Contents
Rolling bearings typically operate under time-varying conditions, which present challenges for fault diagnosis due to the presence of modulation effect and noise component in the bearing vibration signal. This paper introduces a novel feature extraction method that combines complementary ensemble empirical mode decomposition (CEEMD), Teager-Kaiser...
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Full title
Feature extraction based on vibration signal decomposition for fault diagnosis of rolling bearings
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TN_cdi_proquest_journals_2908222722
Permalink
https://devfeature-collection.sl.nsw.gov.au/record/TN_cdi_proquest_journals_2908222722
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ISSN
0268-3768
E-ISSN
1433-3015
DOI
10.1007/s00170-023-12710-5
