期刊
IEEE-ASME TRANSACTIONS ON MECHATRONICS
卷 27, 期 6, 页码 5254-5263出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TMECH.2022.3177174
关键词
Improved loss function; novel joint transfer network (NJTN); simulation domain; unsupervised fault diagnosis; weight allocation mechanism
类别
资金
- National Natural Science Foundation of China [51905160]
- Natural Science Fund for Excellent Young Scholars of Hunan Province [2021JJ20017]
This article proposes a novel joint transfer network for unsupervised bearing fault diagnosis from the simulation domain to the experimental domain. It uses bearing simulation data to construct the source domain, an improved loss function to achieve alignment across domains, and a weight allocation mechanism to suppress negative transfer.
Unsupervised cross-domain fault diagnosis of bearings has practical significance; however, the existing studies still face some problems. For example, transfer diagnosis scenarios are limited to the experimental domain, cross-domain marginal distribution and conditional distribution are difficult to align simultaneously, and each source-domain sample is assigned with equal importance during the domain adaptation process. Aiming at the above mentioned challenges, this article proposes a novel joint transfer network for unsupervised bearing fault diagnosis from the simulation domain to the experimental domain. The sufficient bearing simulation data containing rich fault label information are used to construct the source domain to reduce the dependence on the resources of laboratory test rigs. An improved loss function embedded with joint maximum mean discrepancy is designed to achieve simultaneous alignments of marginal and conditional distributions across domains in unsupervised scenarios. A weight allocation mechanism for each source-domain sample is developed to suppress negative transfer. Two experimental datasets collected from laboratory test rigs are used as the target domains to validate the effectiveness of the proposed method. The results show that the proposed method is superior to other popular unsupervised cross-domain fault diagnosis methods.
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