*Article* **A Weighted Subdomain Adaptation Network for Partial Transfer Fault Diagnosis of Rotating Machinery**

**Sixiang Jia, Jinrui Wang, Xiao Zhang and Baokun Han \***

College of Mechanical and Electronic Engineering, Shandong University of Science and Technology, Qingdao 266590, China; sixiang\_j@163.com (S.J.); wangjr33@163.com (J.W.); zhangxiao9789@163.com (X.Z.) **\*** Correspondence: bk\_han@163.com; Tel.: +86-139-5325-2858

**Abstract:** Domain adaptation-based models for fault classification under variable working conditions have become a research focus in recent years. Previous domain adaptation approaches generally assume identical label spaces in the source and target domains, however, such an assumption may be no longer legitimate in a more realistic situation that requires adaptation from a larger and more diverse source domain to a smaller target domain with less number of fault classes. To address the above deficiencies, we propose a partial transfer fault diagnosis model based on a weighted subdomain adaptation network (WSAN) in this paper. Our method pays more attention to the local data distribution while aligning the global distribution. An auxiliary classifier is introduced to obtain the class-level weights of the source samples, so the network can avoid negative transfer caused by unique fault classes in the source domain. Furthermore, a weighted local maximum mean discrepancy (WLMMD) is proposed to capture the fine-grained transferable information and obtain sample-level weights. Finally, relevant distributions of domain-specific layer activations across different domains are aligned. Experimental results show that our method could assign appropriate weights to each source sample and realize efficient partial transfer fault diagnosis.

**Keywords:** domain adaptation; partial transfer; fault diagnosis; subdomain; rotating machinery

**Citation:** Jia, S.; Wang, J.; Zhang, X.; Han, B. A Weighted Subdomain Adaptation Network for Partial Transfer Fault Diagnosis of Rotating Machinery. *Entropy* **2021**, *23*, 424. https://doi.org/10.3390/e23040424

Academic Editor: Yongbo Li

Received: 12 March 2021 Accepted: 31 March 2021 Published: 1 April 2021

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