An iterative-cluster domain adaptation fault diagnosis method for incremental multi-target domain adaptation

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Abstract
Domain adaptation methods have demonstrated considerable potential in enhancing the performance of fault diagnosis models across diverse operating conditions. While single-target domain adaptation has been extensively explored, its efficacy is reduced in scenarios involving multi-target and incremental-target domains. However, such scenarios are common in real-world projects, necessitating the exploration of more sophisticated, multi-domain, and incremental domain adaptation methods. In response, we propose an iterative-cluster domain adaptation (ICDA) method, which is tailored to address incremental multi-target domain adaptation challenges. On the basis of single-target domain adaptation, ICDA enhances domain alignment through cyclic iteration and clustering algorithms. Notably, it has been demonstrated to be effective in a range of domain adaptation scenarios, including single-target, multi-target, and incremental-target adaptation, even in the unlabeled target domains. The efficacy and superiority of ICDA over baseline models has been substantiated through validation on the CWRU and WINDEY datasets.
Funding Information
  • National Social Science Foundation of China (20FJYB020)

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