Rolling Bearing Fault Diagnosis Using Deep Transfer Learning Based on Joint Generalized Sliced Wasserstein Distance
The big data of rolling bearings for on-site monitoring usually contains very few failure samples and easily affected by noise and monitoring errors, so it is difficult to extract and identify useful fault information in normal samples. In addition, the rolling bearing samples of field test are un-l...
Main Authors: | , , , , |
---|---|
Format: | Article |
Language: | English |
Published: |
IEEE
2024-01-01
|
Series: | IEEE Access |
Subjects: | |
Online Access: | https://ieeexplore.ieee.org/document/10464273/ |