One-dimensional time-frequency dual-channel visual transformer for bearing fault diagnosis under strong noise and limited data conditions

Abstract In industrial settings, bearing health directly affects equipment stability, making accurate and efficient fault diagnosis critical for operational safety. Recently, Transformer models have been widely adopted in bearing fault diagnosis due to their strong global modeling capabilities. Howe...

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Auteurs principaux:	Shaobin Cai, Yuchen Wang, Wanchen Cai, Yuchang Mo, Liansuo Wei
Format:	Artigo
Langue:	Inglês
Publié:	Nature Portfolio 2025-07-01
Collection:	Scientific Reports
Sujets:	Transformer Bearing Fault Diagnosis Deep Learning Self-attention Cross-scale Convolution
Accès en ligne:	https://doi.org/10.1038/s41598-025-12533-2
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