Rolling bearing fault diagnosis of launch vehicle based on adaptive deep CNN

被引：0
|
作者：
Cao J. [1 ]
Wang S. [1 ,2 ]
Yue X. [2 ]
Lei N. [1 ]
机构：
[1] School of Combat Support, Rocket Force University of Engineering, Xi'an
[2] Troop 96884, PLA, Luoyang
来源：
Zhendong yu Chongji/Journal of Vibration and Shock | 2020年 / 39卷 / 05期
关键词：
Convolutional neural network (CNN); Fault diagnosis; Feature learning; Launch vehicle; Particle swarm optimization (PSO); Rolling bearing;
D O I：
10.13465/j.cnki.jvs.2020.05.013
中图分类号：
学科分类号：
摘要：
As key components of launching vehicle, rolling bearings' working conditions usually are very complex to make their fault diagnosis be difficult. Here, in order to effectively perform rolling bearing fault diagnosis, a novel method called the adaptive deep convolutional neural network (CNN) was proposed. Aiming at problems of lower calculation efficiency and parametric adjusting needing manual experience existing in the traditional CNN diagnosis method, PSO algorithm was used to determine structure and parameters of a CNN model. The principal component analysis (PCA) method was used to visualize its fault diagnosis feature learning process, and evaluate its feature learning ability. The diagnosis results with several diagnosis methods, respectively under 10 different bearing working conditions showed that compared with standard CNN, SVM and ANN diagnosis methods, the proposed method has higher diagnosis accuracy and better robustness. © 2020, Editorial Office of Journal of Vibration and Shock. All right reserved.
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页码：97 / 104and149
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