Bearing fault diagnosis based on deep dynamic domain adaptation

被引：0

作者：

Wang J. ^{[1
]}

Lei W. ^{[1
]}

Liu H. ^{[1
]}

Wei L. ^{[1
]}

Han D. ^{[1
]}

机构：

[1] School of Mechanical and Power Engineering, Zhengzhou University, Zhengzhou

来源：

Zhendong yu Chongji/Journal of Vibration and Shock | 2023年 / 42卷 / 14期

关键词：

contribution; dynamic domain adaptation; fault diagnosis; transfer learning;

D O I：

10.13465/j.cnki.jvs.2023.14.029

中图分类号：

学科分类号：

摘要：

Aiming at the problem that the data distributions in source domain and target domain in transfer learning are very different, and it is difficult to adapt to the dynamic changes of edge distribution and conditional distribution in traditional learning, a bearing fault diagnosis method based on deep dynamic domain adaptation was proposed. In the domain adaptation layer, a dynamic distribution adaptation method was introduced, and edge distribution alignment and conditional distribution alignment were performed by domain classifiers, and dynamic domain adaptation was performed by dynamically measuring the contribution of conditional distribution and edge distribution to the domain according to a balance factor. Through the migration diagnosis test and comparative analysis of the bearing data sets of Case Western Reserve University and Jiangnan University under variable working conditions, the accuracy of cross-domain diagnosis was effectively improved, and the effectiveness and excellence of the proposed method were verified. © 2023 Chinese Vibration Engineering Society. All rights reserved.

引用

页码：245 / 250

页数：5

共 13 条

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