Fault diagnosis of rotating machinery based on improved deep residual network

被引：0
|
作者：
Hou Z. [1 ]
Wang H. [1 ]
Zhou L. [1 ]
Fu Q. [1 ]
机构：
[1] School of Civil Aviation, Nanjing University of Aeronautics and Astronautics, Nanjing
来源：
Xi Tong Gong Cheng Yu Dian Zi Ji Shu/Systems Engineering and Electronics | 2022年 / 44卷 / 06期
关键词：
Dropout layer; Fault diagnosis; Improved deep residual network (IDRN); Long short-term memory (LSTM) network;
D O I：
10.12305/j.issn.1001-506X.2022.06.34
中图分类号：
学科分类号：
摘要：
An improved deep residual network (IDRN) for fault diagnosis of rotating machinery is proposed to solve the problems of fault feature extraction difficulty caused by complex and variable working conditions and insufficient samples of labels. Firstly, one-dimensional vibration signals of rotating machinery are collected for data preprocessing. Then, long short-term memory (LSTM) network is introduced on the basis of the deep residual network, in which the time-series information of faults could be captured effectively.The Dropout layer is introduced into the residual block to improve the accuracy and convergence speed of fault diagnosis. Finally, the validity of the proposed method is verified on the data sets of bearings and gears.Experimental results show that there is no obvious network degradation phenomenon when the proposed method is used to stack multi-layer network models. Compared with several widely used diagnostic methods, the proposed method shows higher average diagnostic accuracy and good applicability. © 2022, Editorial Office of Systems Engineering and Electronics. All right reserved.
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页码：2051 / 2059
页数：8
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