Discrete wavelet integrated convolutional residual network for bearing fault diagnosis under noise and variable operating conditions

Abstract Bearing faults in rotating machinery can lead to significant economic losses due to downtime and pose serious safety risks. Accurate fault diagnosis is crucial for effective condition monitoring. Traditional methods for diagnosing bearing faults under noisy conditions often rely on complex...

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Автори:	Yunfeng Ni, Shuang Li, Ping Guo
Формат:	Artigo
Мова:	Inglês
Опубліковано:	Nature Portfolio 2025-05-01
Серія:	Scientific Reports
Предмети:	Rolling bearing Fault diagnosis Residual neural network Cycle learning rate strategy Wavelet downsampling Discrete wavelet transform
Онлайн доступ:	https://doi.org/10.1038/s41598-025-99346-5
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