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

Xiaoxia Liang, Ming Zhang, Guojin Feng, Duo Wang, Yuchun Xu, Fengshou Gu

Research output: Contribution to journalArticlepeer-review


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.
Original languageEnglish
Article number14975
Issue number20
Publication statusPublished - 17 Oct 2023

Bibliographical note

© 2023 by the authors.
Licensee MDPI, Basel, Switzerland.
This article is an open access article
distributed under the terms and
conditions of the Creative Commons
Attribution (CC BY) license (https://

Funding: This research was funded by the RECLAIM project ‘Remanufacturing and Refurbishment
of Large Industrial Equipment’ and received funding from the European Commission Horizon
2020 research and innovation programme under grant agreement No. 869884. This work was also
supported by the Efficiency and Performance Engineering Network International Collaboration Fund
Award 2022 (TEPEN-ICF 2022) project, and the Natural Science Foundation of Hebei (grant No.
E2022202101 and E2022202047).


  • Management, Monitoring, Policy and Law
  • Renewable Energy, Sustainability and the Environment
  • Geography, Planning and Development
  • Building and Construction


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