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Adaptive inter-intradomain alignment network with class-aware sampling strategy for rolling bearing fault diagnosis

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Abstract

Existing unsupervised domain adaptation approaches primarily focus on reducing the data distribution gap between the source and target domains, often neglecting the influence of class information, leading to inaccurate alignment outcomes. Guided by this observation, this paper proposes an adaptive inter-intradomain discrepancy method to quantify the intra-class and inter-class discrepancies between the source and target domains. Furthermore, an adaptive factor is introduced to dynamically assess their relative importance. Building upon the proposed adaptive inter-intradomain discrepancy approach, we develop an inter-intra-domain alignment network with a class-aware sampling strategy (IDAN-CSS) to distill the feature representations. The class-aware sampling strategy, integrated within IDAN-CSS, facilitates more efficient training. Through multiple transfer diagnosis cases, we comprehensively demonstrate the feasibility and effectiveness of the proposed IDAN-CSS model.

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Authors and Affiliations

  1. National Key Discipline Laboratory of Armament Launch Theory & Technology, Rocket Force University of Engineering, Xi’an, 710025, China

    QinHe Gao, Tong Huang, ZhiHao Liu & Dong Wang

  2. Key Laboratory of Road Construction Technology & Equipment Ministry of Education, Chang’an University, Xi’an, 710054, China

    Ke Zhao

  3. College of Mechanical and Vehicle Engineering, Hunan University, Changsha, 410082, China

    HaiDong Shao

  4. Institute of Systems and Robotics, Department of Electrical and Computer Engineering, University of Coimbra, Coimbra, 3030-290, Portugal

    Bo Jin

Authors
  1. QinHe Gao
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  2. Tong Huang
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  3. Ke Zhao
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  4. HaiDong Shao
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  5. Bo Jin
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  6. ZhiHao Liu
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  7. Dong Wang
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Corresponding author

Correspondence to QinHe Gao.

Additional information

This work was supported by the National Natural Science Foundation of China (Grant Nos. 52275104, 51905160) and the Natural Science Fund for Excellent Young Scholars of Hunan Province (Grant No. 2021JJ20017).

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Gao, Q., Huang, T., Zhao, K. et al. Adaptive inter-intradomain alignment network with class-aware sampling strategy for rolling bearing fault diagnosis. Sci. China Technol. Sci. 66, 2862–2870 (2023). https://doi.org/10.1007/s11431-023-2447-4

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  • DOI: https://doi.org/10.1007/s11431-023-2447-4

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