Rolling bearing fault diagnosis based on multi-source domain adaptive residual network

被引：0

作者：

Gao X. ^{[1
,2
,3
,4
]}

Zhang Z. ^{[1
,2
,3
,4
]}

Gao H. ^{[1
,2
,3
,4
]}

Qi Y. ^{[5
]}

机构：

[1] Faculty of Information Technology, Beijing University of Technology, Beijing

[2] MOE Engineering Research Center of Digital Community, Beijing

[3] Beijing Lab for Urban Rail Transit, Beijing

[4] Beijing Municipal Key Lab of Computational Intelligence and Intelligent System, Beijing

[5] School of Electric Power, Inner Mongolia University of Technology, Hohhot

来源：

Zhendong yu Chongji/Journal of Vibration and Shock | 2024年 / 43卷 / 07期

关键词：

domain adaptation; local maximum mean difference (LMMD); multi-source domain adaptive residual network (MDARN); rolling bearing fault diagnosis;

D O I：

10.13465/j.cnki.jvs.2024.07.030

中图分类号：

学科分类号：

摘要：

Here, aiming at the problem of weaker applicability of traditional unsupervised domain adaptation methods in multi-working condition rolling bearing fault diagnosis scenarios, a multi-source domain adaptative residual network (MDARN) was proposed. By aligning relevant subdomains from multiple source domains, MDARN could improve its fault diagnosis performance under multiple working conditions. Firstly, ResNeXt residual network was used to fully extract transferable features from source domain and target domain. Then, the local maximum mean difference (LMMD) criterion was introduced to align relevant subdomains in target domain based on subdomains of two source domains to reduce distribution differences among relevant subdomains and global domain. Finally, the proposed method was experimentally verified using the bearing dataset of Case Western Reserve University, US and the actual bearing vibration dataset generated by MFS mechanical comprehensive fault test bench. The results showed that the average fault diagnosis accuracy of this method under multi-working condition reaches 99. 76%; compared with existing representative methods, the proposed method has better fault diagnosis effect. © 2024 Chinese Vibration Engineering Society. All rights reserved.

引用

页码：290 / 299

页数：9

共 38 条

[21] Zhuang F, Qi Z, Duan K, Et al., A comprehensive survey on transfer learning, Proceedings of the IEEE, 109, 1, pp. 43-76, (2020)
[22] Pan S J, Tsang I W, Kwok J T, Et al., Domain adaptation via transfer component analysis, IEEE transactions on neural networks, 22, 2, pp. 199-210, (2010)
[23] Yang B, Lei Y, Jia F, Et al., A polynomial kernel induced distance metric to improve deep transfer learning for fault diagnosis of machines, IEEE Transactions on Industrial Electronics, 67, 11, pp. 9747-9757, (2019)
[24] Han T, Liu C, Yang W, Et al., Deep transfer network with joint distribution adaptation: A new intelligent fault diagnosis framework for industry application, ISA transactions, 97, pp. 269-281, (2020)
[25] LI L, WAN Z, HE H., Dual alignment for partial domain adaptation [J], IEEE Transactions on Cybernetics, 51, 7, pp. 3404-3416, (2020)
[26] FARAJZADEH-ZANJANI M, HALLAJI E, RAZAVI-FAR R, Et al., Adversarial semi-supervised learning for diagnosing faults and attacks in power grids, IEEE Transactions on Smart Grid, 12, 4, pp. 3468-3478, (2021)
[27] ROY S, KRIVOSHEEV E, ZHONG Z, Et al., Curriculum graph co-teaching for multi-target domain adaptation [C], Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, (2021)
[28] ZHAO C, SHEN W., A domain generalization network combing invariance and specificity towards real-time intelligent fault diagnosis, Mechanical Systems and Signal Processing, 173, (2022)
[29] SZEGEDY C, LIU W, JIA Y, Et al., Going deeper with convolutions [C], Proceedings of the IEEE conference on computer vision and pattern recognition, (2015)
[30] HE K, ZHANG X, REN S, Et al., Deep residual learning for image recognition, Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, (2016)

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