DGAT-LPS

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一种新的基于动态图注意力网络和标签传播策略的半监督故障诊断方法

DGAT-LPS 최신버젼 다운로드

최종 버전 다운로드 (.zip)

// repository documentation

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# 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"](https://www.sciencedirect.com/science/article/abs/pii/S1474034622001124)
* Created by Shen Yan, Haidong Shao, Yiming Xiao, Jian Zhou, Yuandong Xu and Jiafu Wan.
* Journal: Advanced Engineering Informatics
![DGAT-LPS](https://github.com/yanshen0210/DGAT-LPS/blob/main/framwork.jpg)

## 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](https://arxiv.org/abs/2105.14491)
* Special thanks to Li et al. for the GNN base framework provided by [PHMGNNBenchmark](https://github.com/HazeDT/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},
}
```
If our work is useful to you, please star it, it is the greatest encouragement to our open source work, thank you very much!

## Contact
- yanshen0210@gmail.com

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