DGAT-LPS: A new semi-supervised fault diagnosis method called dynamic graph attention network with label propagation strategy

• Core codes for the paper "Semi-supervised fault diagnosis of machinery using LPS-DGAT under speed fluctuation and extremely low labeled rates"
• Created by Shen Yan, Haidong Shao, Yiming Xiao, Jian Zhou, Yuandong Xu and Jiafu Wan.
• Journal: Advanced Engineering Informatics

Our operating environment

• Python 3.8
• torch-geometric 2.2.0
• pytorch 1.10.1
• pandas 1.5.3
• numpy 1.23.5
• and other necessary libs

Guide

• This repository provides a concise framework for semi-supervised fault diagnosis. It includes a demo dataset; the pre-processing and graph composition process for the data and the model proposed in the paper.
• You just need to run train_test_graph.py. You can also adjust the structure and parameters of the model to suit your needs.

Pakages

• data contians a demo dataset
• datasets contians the pre-processing and graph composition process for the data
• models contians the model proposed in the paper

Acknowledgement

• The DGAT is derived from the paper: arXiv:2105.14491
• Special thanks to Li et al. for the GNN base framework provided by PHMGNNBenchmark

Citation

If you use our work as a comparison model, please cite:

@paper{DGAT-LPS,
  title = {Semi-supervised fault diagnosis of machinery using LPS-DGAT under speed  fluctuation and extremely low labeled rates},
  author = {Shen Yan, Haidong Shao, Yiming Xiao, Jian Zhou, Yuandong Xu and Jiafu Wan},
  journal = {Advanced Engineering Informatics},
  volume = {53},
  pages = {101648},
  year = {2022},
  doi = {https://doi.org/10.1016/j.aei.2022.101648},
  url = {https://www.sciencedirect.com/science/article/abs/pii/S1474034622001124},
}

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Contact

• yanshen0210@gmail.com