yanshen0210 / FGDAE-a-machinery-fault-detection-method

一种用于复杂工况机械故障检测的GNN方法

FGDAE: A new machinery anomaly detection method towards complex operating conditions

• Core codes for the paper "FGDAE: A new machinery anomaly detection method towards complex operating conditions"
• Created by Shen Yan, Haidong Shao, Zhishan Min, Jiangji Peng, Baoping Cai, Bin Liu.
• Journal: Reliability Engineering and System Safety

Our operating environment

• Python 3.8
• pytorch 1.10.1
• and other necessary libs

Guide

• This repository provides a concise framework for machinery anomaly detection.
• It includes the pre-processing and graph composition process for the data and the model proposed in the paper.
• We have also integrated 4 baseline methods for comparison.
• Graph_train_val_test.py is the train&val&test process of our proposed method; Base_train_val_test.py is the train&val&test process of base methods.
• You need to load the data in following Datasets link at first, and put them in the data folder. Then run in General_procedure.py
• You can also adjust the structure and parameters of the model to suit your needs.

Datasets

• Case1
• Case2

Run the code

Case1

• General_procedure.py --data_dir "./data/Case1"; --data_num ['200Hz_0N', '300Hz_1000N', '400Hz_1400N'];
  --sensor_number 6; --fault_num 7; --unbalance_train [200, 100, 10]

Case2

• General_procedure.py --data_dir "./data/Case2"; --data_num ['G_20_0', 'G_30_2'];
  --sensor_number 8; --fault_num 5; --unbalance_train [200, 10]

Pakages

• data needs loading the Datasets in above links
• datasets contians the pre-processing and graph composition process for the data
• models contians the proposed model and 4 base models
• utils contians two types of train&val&test processes

Citation

If our work is useful to you, please cite the following paper, it is the greatest encouragement to our open source work, thank you very much!

@paper{FGDAE,
  title = {FGDAE: A new machinery anomaly detection method towards complex operating conditions},
  author = {Shen Yan, Haidong Shao, Zhishan Min, Jiangji Peng, Baoping Cai, Bin Liu},
  journal = {Reliability Engineering and System Safety},
  volume = {236},
  pages = {109319},
  year = {2023},
  doi = {https://doi.org/10.1016/j.ress.2023.109319},
  url = {https://www.sciencedirect.com/science/article/abs/pii/S0951832023002338},
}

Contact

• [e-mail]