A Novel Deep Model with Meta-learning for Rolling Bearing Few-shot Fault Diagnosis

Xiaoxia Liang, Ming Zhang*, Guojin Feng, Yuchun Xu, Dong Zhen, Fengshou Gu

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review


Machine learning, especially deep learning, has been highly successful in data-
intensive applications, however, the performance of these models will drop
significantly when the amount of the training data amount does not meet the
requirement. This leads to the so-called Few-Shot Learning (FSL) problem, which
requires the model rapidly generalize to new tasks that containing only a few labeled samples. In this paper, we proposed a new deep model, called deep convolutional meta-learning networks (DCMLN), to address the low performance of generalization under limited data for bearing fault diagnosis. The essential of our approach is to learn a base model from the multiple learning tasks using a support dataset and finetune the learnt parameters using few-shot tasks before it can adapt to the new learning task based on limited training data. The proposed method was compared to several few-shot learning methods, including methods with and without pre-training the embedding mapping, and methods with finetuning the classifier or the whole model by utilizing the few-shot data from the target domain. The comparisons are carried out on one-shot and ten-shot tasks using the CWRU bearing dataset and a cylindrical roller bearing dataset. The experimental result illustrates that our method has good performance on the bearing fault diagnosis across various few-shot conditions. In addition, we found that the pre-training process does not always improve the prediction accuracy.
Original languageEnglish
Pages (from-to)102–114
Number of pages22
JournalJournal of Dynamics, Monitoring and Diagnostics
Issue number2
Publication statusPublished - 18 Apr 2023

Bibliographical note

Funding: This research was funded by 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. The authors also acknowledge the support of the Efficiency and Performance Engineering Network International Collaboration Fund
Award 2022 (TEPEN-ICF 2022) project “Intelligent Fault Diagnosis Method and
System with Few-Shot Learning Technique under Small Sample Data Condition”

The Journal of Dynamics, Monitoring and Diagnostics applies the Creative Commons Attribution (CC-BY) license to published articles. Under this license, authors retain ownership of the copyright for their content, but they allow anyone to download, reuse, reprint, modify, distribute and/or copy the content as long as the original authors and source are cited. Appropriate attribution can be provided by simply citing the original article.


  • Few-shot learning
  • Meta-learning
  • deep model
  • fault diagnosis
  • Bearing


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