Multi-source Domain Adaptation Intelligent Fault Diagnosis Method Based on Asymmetric Adversarial Training

被引：0
|
作者：
Li, Zhipeng [1 ]
Ma, Tianyu [1 ,2 ,3 ]
Liu, Jinping [2 ,3 ,4 ]
Xiang, Qingsong [1 ]
Tang, Junjie [1 ]
机构：
[1] College of Physics and Electronic Science, Hunan Normal University, Changsha
[2] College of Information Science and Engineering, Hunan Normal University, Changsha
[3] Xiangjiang Laboratory, Changsha
[4] Key Laboratory of Computing and Stochastic Mathe-matics, Hunan Normal University, Changsha
来源：
Jixie Gongcheng Xuebao/Journal of Mechanical Engineering | 2024年 / 60卷 / 18期
关键词：
alignment weights; asymmetric adversarial training; domain adaptation; fault diagnosis; triplet-center loss;
D O I：
10.3901/JME.2024.18.076
中图分类号：
学科分类号：
摘要：
When using the traditional domain adaptation method for cross-condition fault diagnosis of axial flow fan, the source domain and target domain features will move closer to each other, thus changing the trained source domain feature distribution. And when the source domain fault features are gathered at the decision boundary, the target domain fault features are also gathered at the decision boundary after domain adaptation, which is easy to cause misclassification of some target samples. In addition, single source domain adaptation will affect the generalization ability of the model. For the above problems, a multi-source domain adaptation intelligent fault diagnosis method based on asymmetric adversarial training (TC-MAADA) is proposed. The method first uses triplet-center loss to improve the discrimination of target samples by reducing the intra-class distance and increasing the inter-class distance of fault features in the source domain. Then adopts the asymmetric adversarial training to realize the one-way movement of the target domain fault features to the source domain. Finally, the domain-invariant features of different source and target domains are extracted and input to their respective fault classifiers, using the cosine similarity to align the outputs of each classifier while applying alignment weights to improve the cross-domain diagnostic ability of the model. Experiments show that the method is effective in solving relevant practical industrial problems. © 2024 Chinese Mechanical Engineering Society. All rights reserved.
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页码：76 / 88
页数：12
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