Open AccessBearing fault diagnosis based on XWT-CEEMD noise reduction
Author(s) -
Yuqian Wei
Publication year - 2022
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/2196/1/012035
Subject(s) - hilbert–huang transform , computer science , pattern recognition (psychology) , noise reduction , support vector machine , fault (geology) , speech recognition , artificial intelligence , white noise , telecommunications , seismology , geology
In recent years, bearing fault diagnosis has been a research hotspot. In order to improve the reliability of acoustic fault diagnosis, this paper combines Cross Wavelet Transform (XWT) and complementary ensemble empirical mode decomposition (CEEMD) to extract bearing fault features from acoustic signals. Finally, the time-domain features and spectral centroid are input into the SVM for fault classification. The results show that the proposed method can effectively improve the reliability of acoustic fault diagnosis.