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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Hlavní autoři:	Hao Luo, Xinyue Wang, Li Zhang
Médium:	Artigo
Jazyk:	Inglês
Vydáno:	MDPI AG 2025-01-01
Edice:	Actuators
Témata:	fault diagnosis unsupervised domain adaptation class imbalance rolling bearing
On-line přístup:	https://www.mdpi.com/2076-0825/14/1/39
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