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Fault diagnosis method of deep adversarial transfer based on middle bridge layer and similarity matrix

Published:2023-07-18 author:CAI Neng, WU Bing, LI Xiang-yu, et al. Browse: 377 Check PDF documents

Fault diagnosis method of deep adversarial transfer based on middle

bridge layer and similarity matrix

CAI Neng1, WU Bing1, 2, LI Xiang-yu1, 2, LI Cong-ming1,2

(1.College of Mechanical and Vehicle Engineering ,Taiyuan University of Technology, Taiyuan 030024, China;

2.Key Laboratory of New Sensors and Intelligent Control of Ministry of Education, Taiyuan University of

Technology, Taiyuan 030024, China)

Abstract: In view of the existing fault diagnosis algorithms based on deep adversarial transfer learning could not fully learn the common knowledge features which could be used for transfer due to the influence of the rich feature attributes in the domain adaptation, and also the problem that the alignment degree of different categories was different at the category level in the global distribution alignment was ignored, an adversarial fault diagnosis model based on middle bridge layer and similarity matrix(MB-SM)was proposed to fully learn the common knowledge features, and realize the purpose of cross-domain diagnosis and identification of rolling bearing fault. Firstly, the improved one-dimensional multi-scale residual network was used to extract the features of the data. Then the middle bridge layer and similarity matrix were introduced to realize the full learning of the common knowledge features and reduce the difficulty of data transmission in the whole network. It further strengthened the clustering and the separation of categories in the same category in the source domain and target domain, and improved the domain adaptation ability of fault data. Finally, the proposed model method was verified by using laboratory bearing data set and Case Western Reserve University (CWRU) data set. The research results show that the average accuracy of the proposed method is 90.37% on the self-built laboratory data set and is 99.34% on the Case Western Reserve University (CWRU) data set, which can achieve better diagnostic performance than other transfer learning comparison models.

Key words: rolling bearing; fault crossdomain diagnosis and identification; middle bridge layer and similarity matrix(MB-SM); adversarial transfer learning; domain adaptation; deep convolutional neural networks(DCNN)

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