System Development & Application
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App. Res. of Comp. Printed Article
Vol. 39, 2022 No. 7
2098-2103

Bearing fault diagnosis based on dynamic convolution multi-layer domain adaptive

Zhou Huafenga
Cheng Peiyuana
Shao Siyua
Zhao Yuweia
Jiao Xiaoxuanb
a. Air Defense & Missile Defense Academy, b. School of Aeronautical Engineering, Air Force Engineering University, Xi'an 710051, China
DOI: 10.19734/j.issn.1001-3695.2021.11.0652

Abstract

The existing deep learning-based bearing fault diagnosis methods always depend on data, and require that training data and testing data have the same distribution. Under the condition of variable working conditions, the model classification accuracy for fault diagnosis may decrease due to the change of data distribution. In order to ensure that fault diagnosis model can effectively identify the bearing working states under various conditions, this paper proposed a novel intelligent fault diagnosis network, called dynamic convolutional multilayer domain adaptation(DCMDA), which based on the theory of unsupervised domain adaptation. On the one hand, the proposed model made full use of the powerful feature extraction capability of dynamic convolution to extract more effective fault features. On the other hand, the proposed framework used correlation alignment(CORAL) to perform nonlinear transformation, at the same time, it aligned the second-order statistics of the multi-layer fault feature distribution. The proposed model promoted the transfer of diagnostic knowledge from the source domain to the target domain, and improved the fault recognition accuracy without fault labels from the target domain. Experimental verification on two datasets with a total of 14 transfer tasks show that the dynamic convolution multilayer domain adaptation network can achieve effective fault diagnosis with high recognition accuracy.

Foundation Support

国家自然科学基金资助项目
陕西省自然科学基础研究计划资助项目
十四五装发预先研究项目

Publish Information

DOI: 10.19734/j.issn.1001-3695.2021.11.0652
Publish at: Application Research of Computers Printed Article, Vol. 39, 2022 No. 7
Section: System Development & Application
Pages: 2098-2103
Serial Number: 1001-3695(2022)07-028-2098-06

Publish History

[2022-02-16] Accepted Paper
[2022-07-05] Printed Article

Cite This Article

周华锋, 程培源, 邵思羽, 等. 基于动态卷积多层域自适应的轴承故障诊断 [J]. 计算机应用研究, 2022, 39 (7): 2098-2103. (Zhou Huafeng, Cheng Peiyuan, Shao Siyu, et al. Bearing fault diagnosis based on dynamic convolution multi-layer domain adaptive [J]. Application Research of Computers, 2022, 39 (7): 2098-2103. )

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  • Application Research of Computers Monthly Journal
  • Journal ID ISSN 1001-3695
    CN  51-1196/TP

Application Research of Computers, founded in 1984, is an academic journal of computing technology sponsored by Sichuan Institute of Computer Sciences under the Science and Technology Department of Sichuan Province.

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