Conditional distribution-guided adversarial transfer learning network with multi-source domains for rolling bearing fault diagnosis

Zhenghong Wu, Hongkai Jiang, Shaowei Liu, Yunpeng Liu, Wangfeng Yang

科研成果: 期刊稿件文章同行评审

38 引用 (Scopus)

摘要

The application of transfer learning to effectively identify rolling bearing fault has been attracting much attention. Most of the current studies are based on single-source domain or multi-source domains constructed from different working conditions of the same machine. However, in practical scenarios, it is common to obtain multiple source domains from different machines, which brings new challenges to how to use these source domains to complete fault diagnosis. To solve the issue, a conditional distribution-guided adversarial transfer learning network with multi-source domains (CDGATLN) is developed for fault diagnosis of bearing installed on different machines. Firstly, the knowledge of multi-source domains from different machines is transferred to the single target domain by decreasing data distribution discrepancy between each source domain and target domain. Then, a conditional distribution-guided alignment strategy is introduced to decrease conditional distribution discrepancy and calculate the importance per source domain based on the conditional distribution discrepancy, so as to promote the knowledge transfer of each source domain. Finally, a monotone importance specification mechanism is constructed to constrain each importance to ensure that the source domain with low importance will not be discarded, which enables the knowledge of each source domain to participate in the construction of the model. Extensive experimental results verify the effectiveness and superiority of CDGATLN.

源语言英语
文章编号101993
期刊Advanced Engineering Informatics
56
DOI
出版状态已出版 - 4月 2023

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