Transfer Learning for Fault Diagnosis
迁移学习 故障诊断 深度神经网络
This repository is for the transfer learning or domain adaptive with fault diagnosis.
It should be notice that we use the tensorflow 1.15. If one use the lastest tensorflow, there will be some errors.
The paper is as follow:
Citation
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}
}
Contact
If you have any problem about our code, feel free to contact:
[email protected] or [email protected]
or describe your problem in issues