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A Domain Generation Diagnosis Framework for Unseen Conditions Based on Adaptive Feature Fusion and Augmentation

Author

Listed:
  • Tong Zhang

    (Marine Design and Research Institute of China, China State Ship-Building Corporation Limited, Shanghai 200011, China)

  • Haowen Chen

    (Marine Design and Research Institute of China, China State Ship-Building Corporation Limited, Shanghai 200011, China)

  • Xianqun Mao

    (Marine Design and Research Institute of China, China State Ship-Building Corporation Limited, Shanghai 200011, China)

  • Xin Zhu

    (Marine Design and Research Institute of China, China State Ship-Building Corporation Limited, Shanghai 200011, China)

  • Lefei Xu

    (School of Traffic & Transportation Engineering, Central South University, Changsha 410004, China)

Abstract

Emerging deep learning-based fault diagnosis methods have advanced in the current industrial scenarios of various working conditions. However, the prerequisite of obtaining target data in advance limits the application of these models to practical engineering scenarios. To address the challenge of fault diagnosis under unseen working conditions, a domain generation framework for unseen conditions fault diagnosis is proposed, which consists of an Adaptive Feature Fusion Domain Generation Network (AFFN) and a Mix-up Augmentation Method (MAM) for both the data and domain spaces. AFFN is utilized to fuse domain-invariant and domain-specific representations to improve the model’s generalization performance. MAM enhances the model’s exploration ability for unseen domain boundaries. The diagnostic framework with AFFN and MAM can effectively learn more discriminative features from multiple source domains to perform different generalization tasks for unseen working loads and machines. The feasibility of the proposed unseen conditions diagnostic framework is validated on the SDUST and PU datasets and achieved peak diagnostic accuracies of 94.15% and 93.27%, respectively.

Suggested Citation

  • Tong Zhang & Haowen Chen & Xianqun Mao & Xin Zhu & Lefei Xu, 2024. "A Domain Generation Diagnosis Framework for Unseen Conditions Based on Adaptive Feature Fusion and Augmentation," Mathematics, MDPI, vol. 12(18), pages 1-15, September.
    • Handle: RePEc:gam:jmathe:v:12:y:2024:i:18:p:2865-:d:1478335
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    References listed on IDEAS

    as
    1. Zhao, Chao & Shen, Weiming, 2022. "Adaptive open set domain generalization network: Learning to diagnose unknown faults under unknown working conditions," Reliability Engineering and System Safety, Elsevier, vol. 226(C).
    2. Xiang Li & Wei Zhang & Qian Ding & Jian-Qiao Sun, 2020. "Intelligent rotating machinery fault diagnosis based on deep learning using data augmentation," Journal of Intelligent Manufacturing, Springer, vol. 31(2), pages 433-452, February.
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