Application of convolutional neural network and kurtosis in fault diagnosis of rolling bearing

被引：0
|
作者：
Li J. [1 ]
Liu Y. [1 ]
Yu Y. [1 ]
机构：
[1] College of Power Engineering, Naval University of Engineering, Wuhan
来源：
Hangkong Dongli Xuebao/Journal of Aerospace Power | 2019年 / 34卷 / 11期
关键词：
Convolution neural network; Deep learning; Fault diagnosis; Kurtosis; Rolling bearing;
D O I：
10.13224/j.cnki.jasp.2019.11.014
中图分类号：
学科分类号：
摘要：
Traditional intelligent diagnosis method relying much on expert knowledge and manual extraction data features takes a lot of work. Based on the advantages of deep learning in feature extraction and processing of big data, a method of rolling bearing fault diagnosis based on convolution neural network and kurtosis was studied. This method was used to analyse four kinds of vibration signal of the normal state, the inner race fault, the outer race fault and the ball fault. The vibration signal was processed in segments to obtain kurtosis, which was converted into gray images by data-to-image method. Finally, these were fed into convolution neural network model to complete rolling bearing fault classification. In the case of rolling bearing fault diagnosis, the improved model had a diagnostic accuracy of 99.5%, which was higher than 95.8% of the traditional support vector machine (SVM) algorithm. © 2019, Editorial Department of Journal of Aerospace Power. All right reserved.
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页码：2423 / 2431
页数：8
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