Bearing fault diagnosis based on the combined use of RepVGG and CapsNet

被引：0

作者：

Yin, Aijun ^{[1
]}

Lu, Mingyang ^{[1
]}

Yang, Minying ^{[2
]}

Chen, Xiaomin ^{[1
]}

机构：

[1] College of Mechanical and Vehicle Engineering, Chongqing University, Chongqing

[2] Xi'an Satellite Control Center, Xi'an

来源：

Zhendong yu Chongji/Journal of Vibration and Shock | 2024年 / 43卷 / 14期

关键词：

bearing; convolutional neural network; deep learning; fault diagnosis;

D O I：

10.13465/j.cnki.jvs.2024.14.035

中图分类号：

学科分类号：

摘要：

How to improve feature extraction ability and extract the spatial information of features is the key to achieve high-precision fault diagnosis. RepVGG and other deep convolutional neural networks ignore the spatial information of features, while CapsNet has limited feature extraction ability due to its shallow network level. In order to solve the above problems, a bearing fault diagnosis method based on the combined use of RepVGG and CapsNet was proposed. First, the vibration signals at different measuring points were obtained, converted into two-dimensional feature maps by gramian angular difference field, and concatenated along the channel direction. Then, the RepVGG network was selected as the pre-convolutional layer to realize the feature extraction and the fusion of multi-dimensional vibration signals. Finally, by CapsNet, the features ' spatial information was extracted to realize bearing fault diagnosis. The experimental results show that the fault diagnosis method based on the combined use of RepVGG and CapsNet has excellent fault recognition performance and noise resistance. © 2024 Chinese Vibration Engineering Society. All rights reserved.

引用

页码：301 / 307

页数：6

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