Rolling Bearing Fault Diagnosis Using Deep Transfer Learning Based on Joint Generalized Sliced Wasserstein Distance

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...

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Bibliografische gegevens
Hoofdauteurs: Na Lei, Jipeng Cui, Jicheng Han, Xian Chen, Youfu Tang
Formaat: Artikel
Taal:English
Gepubliceerd in: IEEE 2024-01-01
Reeks:IEEE Access
Onderwerpen:
Fault diagnosis
rolling bearing
transfer learning
generalized sliced Wasserstein distance
Online toegang:https://ieeexplore.ieee.org/document/10464273/

Internet

https://ieeexplore.ieee.org/document/10464273/

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