A modified transformer based on adaptive frequency enhanced attention, large kernel convolution, and multiscale implementation for bearing fault diagnosis

Abstract Bearing fault diagnosis has attracted increasing attention due to its critical role in monitoring the health of rotating machinery. Data-driven models based on deep learning (DL) have demonstrated strong capabilities in feature extraction. However, their performance often degrades under str...

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Detalles Bibliográficos

Principais autores:	Xiao Chang, Shaobin Cai, Wanchen Cai, Yuchang Mo, Liansuo Wei
Formato:	Artigo
Idioma:	Inglês
Publicado:	Nature Portfolio 2025-09-01
Series:	Scientific Reports
Assuntos:	Rolling bearing fault diagnosis Multiscale convolution Adaptive Frequency-Domain attention Noise robustness
Acceso en liña:	https://doi.org/10.1038/s41598-025-18187-4
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