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

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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Bibliographic Details
Main Authors: Shaobin Cai, Yuchen Wang, Wanchen Cai, Yuchang Mo, Liansuo Wei
Format: Article
Language:English
Published: Nature Portfolio 2025-07-01
Series:Scientific Reports
Subjects:
Transformer
Bearing Fault Diagnosis
Deep Learning
Self-attention
Cross-scale Convolution
Online Access:https://doi.org/10.1038/s41598-025-12533-2
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