Bearing fault diagnosis based on information fusion and improved residual dense networks

被引：0

作者：

Yuan C. ^{[1
]}

Sun J. ^{[1
,2
]}

Wen J. ^{[3
]}

Shi P. ^{[3
]}

Yan S. ^{[1
]}

机构：

[1] School of Information Science and Engineering, Yanshan University, Qinhuangdao

[2] Hebei Key Laboratory of Information Transmission and Signal Processing, Yanshan University, Qinhuangdao

[3] Key Laboratory of Measurement Technology and Instrumentation of Hebei Province, Yanshan University, Qinhuangdao

来源：

Zhendong yu Chongji/Journal of Vibration and Shock | 2022年 / 41卷 / 04期

关键词：

Attention mechanism; Bearing fault diagnosis; Multiple transformation domain processing; Residual network;

D O I：

10.13465/j.cnki.jvs.2022.04.026

中图分类号：

学科分类号：

摘要：

Aiming at the problem that the original time signal of a rolling bearing is relatively simple and the features extracted by convolution neural network are different during useful information transmission, a bearing fault diagnosis method was proposed based on multi-domain information fusion and an improved residual dense network. In order to obtain the multifaceted information of fault signal, the original data are transformed in multiple domains. Then the multi-domain information is input into the residual dense network improved by convolution attention mechanism for deep learning. Discrimination is realized according to the importance of the extracted features. The training speed and efficiency of the neural network were improved. Experimental results and analysis show that the proposed method can extract more comprehensive features and has higher recognition accuracy than traditional methods. © 2022, Editorial Office of Journal of Vibration and Shock. All right reserved.

引用

页码：200 / 208and252

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