This repository is for the transfer learning or domain adaptive with fault diagnosis.
The paper is as follow:
If you use this code and datasets for your research, please consider citing:
@inproceedings{zhang2019domain,
title={Domain Adaptation with Multilayer Adversarial Learning for Fault Diagnosis of Gearbox under Multiple Operating Conditions},
author={Zhang, Ming and Lu, Weining and Yang, Jun and Wang, Duo and Bin, Liang},
booktitle={2019 Prognostics and System Health Management Conference (PHM-Qingdao)},
pages={1--6},
year={2019},
organization={IEEE}
}
@ARTICLE{8713860,
author={M. {Zhang} and D. {Wang} and W. {Lu} and J. {Yang} and Z. {Li} and B. {Liang}},
journal={IEEE Access},
title={A Deep Transfer Model With Wasserstein Distance Guided Multi-Adversarial Networks for Bearing Fault Diagnosis Under Different Working Conditions},
year={2019},
volume={7},
number={},
pages={65303-65318},
keywords={Fault diagnosis;Rolling bearings;Data models;Wavelength division multiplexing;Convolution;Employee welfare;Training;Transfer learning;fault diagnosis;convolutional neural network;multi-adversarial networks},
doi={10.1109/ACCESS.2019.2916935},
ISSN={2169-3536},
month={},}
@article{zhang2017research,
title={Research on variational mode decomposition in rolling bearings fault diagnosis of the multistage centrifugal pump},
author={Zhang, Ming and Jiang, Zhinong and Feng, Kun},
journal={Mechanical Systems and Signal Processing},
volume={93},
pages={460--493},
year={2017},
publisher={Elsevier}
}
If you have any problem about our code, feel free to contact:
or describe your problem in issues