Normalization-Guided and Gradient-Weighted Unsupervised Domain Adaptation Network for Transfer Diagnosis of Rolling Bearing Faults Under Class Imbalance

Normalization-Guided and Gradient-Weighted Unsupervised Domain Adaptation Network for Transfer Diagnosis of Rolling Bearing Faults Under Class Imbalance

Transfer learning has garnered significant interest in the field of bearing fault diagnosis under varying operational conditions due to its robust generalization capabilities. However, real-world diagnostic scenarios frequently encounter data imbalances, which complicates the learning of the classif...

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Bibliographic Details
Main Authors: Hao Luo, Xinyue Wang, Li Zhang
Format: Article
Language:English
Published: MDPI AG 2025-01-01
Series:Actuators
Subjects:
fault diagnosis
unsupervised domain adaptation
class imbalance
rolling bearing
Online Access:https://www.mdpi.com/2076-0825/14/1/39
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