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

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...

Full description

Saved in:
Bibliographic Details
Main Authors: Qingming Hu, Xinjie Fu, Dandan Sun, Donghui Xu, Yanqi Guan
Format: Article
Language:English
Published: IEEE 2024-01-01
Series:IEEE Access
Subjects:
Depth-wise separable convolutions
fault diagnosis
image classification
network pruning
Online Access:https://ieeexplore.ieee.org/document/10634161/
Tags: Add Tag
No Tags, Be the first to tag this record!

Internet

https://ieeexplore.ieee.org/document/10634161/

Similar Items