Intelligent Fault Diagnosis for Cross-Domain Few-Shot Learning of Rotating Equipment Based on Mixup Data Augmentation

Existing fault diagnosis methods assume the identical distribution of training and test data, failing to adapt to source–target domain differences in industrial scenarios and limiting generalization. They also struggle to explore inter-domain correlations with scarce labeled target samples, leading...

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Autores principales:	Kun Yu, Yan Li, Qiran Zhan, Yongchao Zhang, Bin Xing
Formato:	Artigo
Lenguaje:	Inglês
Publicado:	MDPI AG 2025-09-01
Colección:	Machines
Materias:	cross-domain few-shot learning Mixup data augmentation self-attention mechanism fault diagnosis
Acceso en línea:	https://www.mdpi.com/2075-1702/13/9/807
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