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Few-Shot Learning Approaches for Fault Diagnosis Using Vibration Data: A Comprehensive Review

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  • Xiaoxia Liang

    (College of Mechanical Engineering, Hebei University of Technology, Tianjin 300401, China
    Advanced Equipment Research Institute Co., Ltd. of HEBUT, Tianjin 300401, China)

  • Ming Zhang

    (College of Engineering and Physical Sciences, Aston University, Birmingham B4 7ET, UK)

  • Guojin Feng

    (College of Mechanical Engineering, Hebei University of Technology, Tianjin 300401, China
    Advanced Equipment Research Institute Co., Ltd. of HEBUT, Tianjin 300401, China)

  • Duo Wang

    (Beijing Institute of Control Engineering, Beijing 100190, China)

  • Yuchun Xu

    (College of Engineering and Physical Sciences, Aston University, Birmingham B4 7ET, UK)

  • Fengshou Gu

    (Centre for Efficiency and Performance Engineering, University of Huddersfield, Huddersfield HD1 3DH, UK)

Abstract

Fault detection and diagnosis play a crucial role in ensuring the reliability and safety of modern industrial systems. For safety and cost considerations, critical equipment and systems in industrial operations are typically not allowed to operate in severe fault states. Moreover, obtaining labeled samples for fault diagnosis often requires significant human effort. This results in limited labeled data for many application scenarios. Thus, the focus of attention has shifted towards learning from a small amount of data. Few-shot learning has emerged as a solution to this challenge, aiming to develop models that can effectively solve problems with only a few samples. This approach has gained significant traction in various fields, such as computer vision, natural language processing, audio and speech, reinforcement learning, robotics, and data analysis. Surprisingly, despite its wide applicability, there have been limited investigations or reviews on applying few-shot learning to the field of mechanical fault diagnosis. In this paper, we provide a comprehensive review of the relevant work on few-shot learning in mechanical fault diagnosis from 2018 to September 2023. By examining the existing research, we aimed to shed light on the potential of few-shot learning in this domain and offer valuable insights for future research directions.

Suggested Citation

  • Xiaoxia Liang & Ming Zhang & Guojin Feng & Duo Wang & Yuchun Xu & Fengshou Gu, 2023. "Few-Shot Learning Approaches for Fault Diagnosis Using Vibration Data: A Comprehensive Review," Sustainability, MDPI, vol. 15(20), pages 1-17, October.
    • Handle: RePEc:gam:jsusta:v:15:y:2023:i:20:p:14975-:d:1261629
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    References listed on IDEAS

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    1. ZhenHua Li & Yujie Zhang & Ahmed Abu-Siada & Xingxin Chen & Zhenxing Li & Yanchun Xu & Lei Zhang & Yue Tong, 2021. "Fault Diagnosis of Transformer Windings Based on Decision Tree and Fully Connected Neural Network," Energies, MDPI, vol. 14(6), pages 1-14, March.
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