Rolling Bearing Fault Diagnosis Based on One-Dimensional Dilated Convolution Network with Residual Connection

doi:10.1109/ACCESS.2021.3059761

	Rolling Bearing Fault Diagnosis Based on One-Dimensional Dilated Convolution Network with Residual Connection
	Liang, Haopeng 3; Zhao, Xiaoqiang 1,2,3
刊名	IEEE Access
	2021
卷号	9 页码:31078-31091
关键词	Convolution Failure analysis Fault detection Multilayer neural networks Time domain analysis Connection structures Convolution neural network Feature learning Noisy environment Residual structure Rolling bearings Time-domain signal Weight coefficients
ISSN号	2169-3536
DOI	10.1109/ACCESS.2021.3059761
英文摘要	As the rolling bearing is the most important part of rotating machinery, its fault diagnosis has been a research hotspot. In order to diagnose the faults of rolling bearing under different noisy environments and different load domains, a new method named one-dimensional dilated convolution network with residual connection is proposed in this paper. The proposed method uses the one-dimensional time-domain signals of rolling bearing as input. Zigzag dilated convolution is introduced into convolution neural network, which can effectively improve the receptive field of the convolutional layer. A multi-level residual connection structure with different weight coefficients is constructed, so that the lower layer features of convolution neural network can be transferred to the upper layer, which improves the feature learning ability. Moreover, in order to enhance the useful features and weaken the useless features, we add the attention module Squeeze-and-Excitation (SE) block after each sub-residual structure. By using the rolling bearing datasets, the experimental results show that the proposed method can effectively diagnose faults of rolling bearing under different noisy environments and different load domains. Compared with other methods, the proposed method has higher accuracy. © 2013 IEEE.
WOS研究方向	Computer Science ; Engineering ; Telecommunications
语种	英语
出版者	Institute of Electrical and Electronics Engineers Inc.
WOS记录号	WOS:000622085300001
内容类型	期刊论文
源URL	[http://ir.lut.edu.cn/handle/2XXMBERH/147738]
专题	电气工程与信息工程学院
作者单位	1.National Experimental Teaching Center of Electrical and Control Engineering, Lanzhou University of Technology, Lanzhou; 730050, China 2.Key Laboratory of Gansu Advanced Control for Industrial Process, Lanzhou University of Technology, Lanzhou; 730050, China; 3.College of Electrical and Information Engineering, Lanzhou University of Technology, Lanzhou, China;
推荐引用方式 GB/T 7714	Liang, Haopeng,Zhao, Xiaoqiang. Rolling Bearing Fault Diagnosis Based on One-Dimensional Dilated Convolution Network with Residual Connection[J]. IEEE Access,2021,9:31078-31091.
APA	Liang, Haopeng,&Zhao, Xiaoqiang.(2021).Rolling Bearing Fault Diagnosis Based on One-Dimensional Dilated Convolution Network with Residual Connection.IEEE Access,9,31078-31091.
MLA	Liang, Haopeng,et al."Rolling Bearing Fault Diagnosis Based on One-Dimensional Dilated Convolution Network with Residual Connection".IEEE Access 9(2021):31078-31091.