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 1; Zhao, Xiaoqiang1,2,3
	2021
发表期刊	IEEE Access
ISSN	2169-3536
卷号	9 页码:31078-31091
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.
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
DOI	10.1109/ACCESS.2021.3059761
收录类别	EI ; SCIE
语种	英语
WOS研究方向	Computer Science ; Engineering ; Telecommunications
WOS类目	Computer Science, Information Systems ; Engineering, Electrical & Electronic ; Telecommunications
WOS记录号	WOS:000622085300001
出版者	Institute of Electrical and Electronics Engineers Inc.
EI入藏号	20210809960420
EI主题词	Roller bearings
EI分类号	601.2 Machine Components ; 716.1 Information Theory and Signal Processing ; 921 Mathematics
来源库	WOS
引用统计	被引频次：22[WOS] [WOS记录] [WOS相关记录]
文献类型	期刊论文
条目标识符	https://ir.lut.edu.cn/handle/2XXMBERH/147738
专题	电气工程与信息工程学院
通讯作者	Zhao, Xiaoqiang
作者单位	1.Lanzhou Univ Technol, Coll Elect & Informat Engn, Lanzhou 730050, Peoples R China; 2.Lanzhou Univ Technol, Key Lab Gansu Adv Control Ind Proc, Lanzhou 730050, Peoples R China; 3.Lanzhou Univ Technol, Natl Expt Teaching Ctr Elect & Control Engn, Lanzhou 730050, Peoples R China
第一作者单位	电气工程与信息工程学院
通讯作者单位	电气工程与信息工程学院; 兰州理工大学
第一作者的第一单位	电气工程与信息工程学院
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.

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