Improved Adversarial Transfer Network for Bearing Fault Diagnosis under Variable Working Conditions

Improved Adversarial Transfer Network for Bearing Fault Diagnosis under Variable Working Conditions

Bearings are one of the critical components of rotating machinery, and their failure can cause catastrophic consequences. In this regard, previous studies have proposed a variety of intelligent diagnosis methods. Most existing bearing fault diagnosis methods implicitly assume that the training and t...

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Bibliographic Details
Main Authors: Jun Wang, Hosameldin Ahmed, Xuefeng Chen, Ruqiang Yan, Asoke K. Nandi
Format: Article
Language:English
Published: MDPI AG 2024-03-01
Series:Applied Sciences
Subjects:
bearing intelligent diagnosis
adversarial learning
transfer learning
improved adversarial transfer network
Online Access:https://www.mdpi.com/2076-3417/14/6/2253

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https://www.mdpi.com/2076-3417/14/6/2253

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