classifier-domain / PDA-Fault-Diagnosis

PDA场景下的旋转机械故障诊断，

PDA-Fault-Diagnosis

"Duplex Adversarial Domain Discriminative Network for Cross-Domain Partial Transfer Fault Diagnosis" paper's pytorch code

Prerequisites:

• python == 3.9
• pytorch ==1.1.0
• torchvision == 0.3.0
• numpy, scipy, PIL, argparse, tqdm

Dataset:

Please manually download the datasets CWRU(https://engineering.case.edu/bearingdatacenter/download-data-file)

Citation

If you find this code useful for your research, please cite our paper

@inproceedings{liu2023duplex,     title={Duplex adversarial domain discriminative network for cross-domain partial transfer fault diagnosis},
    author={Liu, Fuqiang and Deng, Wenlong and Duan, Chaoqun and Qin, Yi and Luo, Jun and Pu, Huayan},
    journal={Knowledge-Based Systems},
    volume={279},     pages={110960},     year={2023},     publisher={Elsevier}

}

Acknowledgement

Some parts of this project are built based on the following open-source implementation

• BA3US [https://github.com/tim-learn/BA3US]
• thuml [https://github.com/thuml/Transfer-Learning-Library]