A Lightweight Rolling Bearing Fault Diagnosis Method Based on Multiscale Depth-Wise Separable Convolutions and Network Pruning

Fault diagnosis in rolling bearings is critical important in preventing machinery damage. Current deep learning-based approaches for rolling bearing fault diagnosis mainly rely on complex models that require significant hardware storage and computing power. In this paper, we introduce a multiscale D...

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Detalles Bibliográficos

Principais autores:	Qingming Hu, Xinjie Fu, Dandan Sun, Donghui Xu, Yanqi Guan
Formato:	Artigo
Idioma:	Inglês
Publicado:	IEEE 2024-01-01
Series:	IEEE Access
Assuntos:	Depth-wise separable convolutions fault diagnosis image classification network pruning
Acceso en liña:	https://ieeexplore.ieee.org/document/10634161/
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