Fault diagnosis of rolling bearings based on a multi branch depth separable convolutional neural network

被引：0
|
作者：
Liu H. [1 ,2 ]
Yao D. [1 ,2 ]
Yang J. [1 ,2 ]
Zhang J. [3 ]
机构：
[1] School of Mechanical-Electronic and Vehicle Engineering, Beijing University of Civil Engineering Architecture, Beijing
[2] Beijing Key Laboratory of Performance Guarantee on Urban Rail Transit Vehicles, Beijing University of Civil Engineering and Architecture, Beijing
[3] Beijing Mass Transit Railway Operation Corporation Ltd., Beijing
来源：
Zhendong yu Chongji/Journal of Vibration and Shock | 2021年 / 40卷 / 10期
关键词：
Anti-noise; Convolutional neural network(CNN); Fault degree; Fault diagnosis; Rolling bearing;
D O I：
10.13465/j.cnki.jvs.2021.10.013
中图分类号：
学科分类号：
摘要：
Aiming at the disadvantages of traditional rolling bearing fault diagnosis methods, such as poor robust, need for artificial feature extraction, large amount of computation, and high requirements for the running equipment, a fault diagnosis method for rolling bearings based on a multi branch depth seperable convolutional neural network (MBDS-CNN) was proposed. Using the depth separable convolution and weight pruning technology to compress the model size, the multi-branch structure ensures the accuracy of the model and avoids the phenomenon of gradient disappearance. The model was evaluated by using a test set, using the model size, diagnostic accuracy, and prediction speed as evaluation indicators. The experimental results show that the fault diagnosis method for rolling bearings based on the MBDS-CNN can effectively identify the fault degree of different parts of the bearing in the noise environment, improve the diagnostic efficiency and reduce the performance requirements of the running equipment. © 2021, Editorial Office of Journal of Vibration and Shock. All right reserved.
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页码：95 / 102
页数：7
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