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Federated Transfer Fault Diagnosis Method Based on Variational Auto-Encoding with Few-Shot Learning

Author

Listed:
  • Yang Ge

    (School of Mechanical Engineering, Changshu Institute of Technology, Changshu 215500, China)

  • Yong Ren

    (School of Mechanical Engineering, Changshu Institute of Technology, Changshu 215500, China)

Abstract

Achieving accurate equipment fault diagnosis relies heavily on the availability of extensive, high-quality training data, which can be difficult to obtain, particularly for models with new equipment. The challenge is further compounded by the need to protect sensitive data during the training process. This paper introduces a pioneering federated transfer fault diagnosis method that integrates Variational Auto-Encoding (VAE) for robust feature extraction with few-shot learning capabilities. The proposed method adeptly navigates the complexities of data privacy, diverse working conditions, and the cross-equipment transfer of diagnostic models. By harnessing the generative power of VAE, our approach extracts pivotal features from signals, effectively curbing overfitting during training, a common issue when dealing with limited fault samples. We construct a federated learning model comprising an encoder, variational feature generator, decoder, classifier, and discriminator, fortified with an advanced training strategy that refines federated averaging and incorporates regularization when handling non-independent data distributions. This strategy ensures the privacy of data while enhancing the model’s ability to discern subtleties in fault signatures across different equipment and operational settings. Our experiments, conducted across various working conditions and devices, demonstrate that our method significantly outperforms traditional federated learning techniques in terms of fault recognition accuracy. The innovative integration of VAE within a federated learning framework not only bolsters the model’s adaptability and accuracy but also upholds stringent data privacy standards.

Suggested Citation

  • Yang Ge & Yong Ren, 2024. "Federated Transfer Fault Diagnosis Method Based on Variational Auto-Encoding with Few-Shot Learning," Mathematics, MDPI, vol. 12(13), pages 1-18, July.
  • Handle: RePEc:gam:jmathe:v:12:y:2024:i:13:p:2142-:d:1431005
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    References listed on IDEAS

    as
    1. Zhang, Wei & Wang, Ziwei & Li, Xiang, 2023. "Blockchain-based decentralized federated transfer learning methodology for collaborative machinery fault diagnosis," Reliability Engineering and System Safety, Elsevier, vol. 229(C).
    2. Shuai Hou & Jizhe Lu & Enguo Zhu & Hailong Zhang & Aliaosha Ye & Zhihan Lv, 2022. "A Federated Learning-Based Fault Detection Algorithm for Power Terminals," Mathematical Problems in Engineering, Hindawi, vol. 2022, pages 1-10, July.
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