Few-shot learning for bearing fault diagnosis pytorch

📖 1. Introduction

A public repository branch of deep transfer learning fault diagnosis, including popular few-shot learning algorithms implemented for bearing fault diagnosis problems. For domain adaptation based methods, see the GitHub repository: fault-diagnosis-transfer-learning-pytorch

For further introductions to transfer learning and few-shot learning in bearing fault diagnosis, please read our paper. And if you find this repository useful and use it in your works, please cite our paper, thank you~:

@ARTICLE{10042467,
  author={Chen, Xiaohan and Yang, Rui and Xue, Yihao and Huang, Mengjie and Ferrero, Roberto and Wang, Zidong},
  journal={IEEE Transactions on Instrumentation and Measurement}, 
  title={Deep Transfer Learning for Bearing Fault Diagnosis: A Systematic Review Since 2016}, 
  year={2023},
  volume={72},
  number={},
  pages={1-21},
  doi={10.1109/TIM.2023.3244237}}

📋 2. To Do

• Siamese Networks (ICML 2015)
• Prototypical Networks (NeurIPS 2017)
• Matching Networks (NeurIPS 2016)
• Relation Networks

📦 3. Requirements

• Python 3.9.12
• Numpy 1.23.1
• torchvision 0.13.0
• Pytorch 1.12.0
• tqdm 4.46.0

👝 4. Dataset

• CWRU

Data structure please refer to fault-diagnosis-transfer-learning-pytorch

📺 5. Usage

• Siamese Networks 10-way 1-shot experiment

python3 Siamese.py --support 300 --backbone "CNN1D" --s_load 3 --t_load 2

• Prototypical Networks 10-way 10-shot experiment

python3 Prototypical.py --n_train 800 --s_load 3 --t_load 2 --support 10 --query 10

🔦 6. Results

🏕️ 7. See also

GitHub: fault-diagnosis-transfer-learning-pytorch