Digital twin-assisted imbalanced fault diagnosis framework using subdomain adaptive mechanism and margin-aware regularization

Core codes for the paper:
"Digital twin-assisted imbalanced fault diagnosis framework using subdomain adaptive mechanism and margin-aware regularization"
Created by Shen Yan, Xiang Zhong, Haidong Shao, Yuhang Ming, Chao Liu, Bin Liu.
Journal: Reliability Engineering and System Safety

Our operating environment

Python 3.8
pytorch 1.10.1
numpy 1.22.0 (If you get an error when saving data, try lowering your numpy version!)
and other necessary libs

Datasets

Save dataset
~~[ADAMS gearbox]~~
(We are very sorry that the simulation data cannot open for the time being because the sharing right is not obtained)
SEU gearbox
XJTU gearbox

Guide

This repository provides a concise framework for imbalanced fault diagnosis.
It includes the pre-processing for the data and the model proposed in the paper.
We have also integrated 8 baseline methods including 4 data-level and 4 algorithm-level methods for comparison.
train_test.py is the train&test process of our proposed method; train_test_base.py is the train&test process of 8 baseline methods.
You need to load the Save dataset in above Datasets link at first, and put them in the data folder.
Then set --save_dataset (in args_diagnosis.py) to False and run in args_diagnosis.py.
You can also choose the modules or adjust the parameters of the model to suit your needs.

Run the code

The proposed method

args_diagnosis.py --transfer_task ADAMS_SEU or ADAMS_XJTU; --transfer_loss SAM+MAR; --save_dataset False

data-level methods

args_diagnosis.py --transfer_task SEU or XJTU; --SMOTETomek True; --gan False; --gen_data False; --save_dataset False
args_diagnosis.py --transfer_task SEU or XJTU; --SMOTETomek False; --gan True; --gen_data True;
--gan_model ACGAN or VAE_GAN or WGAN_GP; --save_dataset False

algorithm-level methods

args_diagnosis.py --transfer_task SEU or XJTU; --SMOTETomek False; --gan False; --gen_data False; --cost_loss True;
--loss WL or FL or DWBL or CBL; --save_dataset False

Pakages

data needs loading the Datasets in above links
datasets contians the pre-processing process for the data
gans contians three gan models as baselines
loss contians four types of loss way
models contians the ResNet18 network as the feature extractor
utils contians two types of train&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{
  title = {Digital twin-assisted imbalanced fault diagnosis framework using subdomain adaptive mechanism and margin-aware regularization},
  author = {Shen Yan, Xiang Zhong, Haidong Shao, Yuhang Ming, Chao Liu},
  journal = {Reliability Engineering and System Safety},
  volume = {239},
  pages = {109522},
  year = {2023},
  doi = {https://doi.org/10.1016/j.ress.2023.109522},
  url = {https://www.sciencedirect.com/science/article/pii/S0951832023004362},
}

Contact

yanshen0210@gmail.com

xingwangjiang/Digital-twin-assisted-imbalanced-fault-diagnosis-framework