Unknown-class recognition adversarial network for open set domain adaptation fault diagnosis of rotating machinery

Ke Wu, Wei Xu, Qiming Shu, Wenjun Zhang, Xiaolong Cui and Jun Wu ()
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Ke Wu: Huazhong University of Science and Technology
Wei Xu: The Second Ship Design and Research Institute of Wuhan
Qiming Shu: Huazhong University of Science and Technology
Wenjun Zhang: The Second Ship Design and Research Institute of Wuhan
Xiaolong Cui: The Second Ship Design and Research Institute of Wuhan
Jun Wu: Huazhong University of Science and Technology

Journal of Intelligent Manufacturing, 2025, vol. 36, issue 5, No 5, 3049 pages

Abstract: Abstract Transfer learning methods have received abundant attention and extensively utilized in cross-domain fault diagnosis, which suppose that the label sets in the source and target domains are coincident. However, the open set domain adaptation problem which include new fault modes in the target domain is not well solved. To address the problem, an unknown-class recognition adversarial network (UCRAN) is proposed for the cross-domain fault diagnosis. Specifically, a three-dimensional discriminator is designed to conduct domain-invariant learning on the source domain, target known domain and target unknown domain. Then, an entropy minimization is introduced to determine the decision boundaries. Finally, a posteriori inference method is developed to calculate the open set recognition weight, which are used to adaptively weigh the importance between known class and unknown class. The effectiveness and practicability of the proposed UCRAN is validated by a series of experiments. The experimental results show that compared to other existing methods, the proposed UCRAN realizes better diagnosis performance in different domain transfer task.

Keywords: Adversarial network; Open set domain adaptation; Unknown-class recognition; Fault diagnosis (search for similar items in EconPapers)
Date: 2025
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DOI: 10.1007/s10845-024-02395-2

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