Tensorflow Code for our paper "Multi-mode data augmentation and fault diagnosis of rotating machinery using modified ACGAN designed with new framework".
Paper Link: (https://doi.org/10.1016/j.aei.2022.101552)
If you find our work useful in your research, please consider citing:
@inproceedings{MACGAN-for-fault-diagnosis-of-rotating-machinery,
title={Multi-mode data augmentation and fault diagnosis of rotating machinery using modified ACGAN designed with new framework},
author={Wei Li, Xiang Zhong, Haidong Shao, Baoping Cai, and Xingkai Yang},
booktitle={Advanced Engineering Informatics},
year={2022}
}
- Python 3.8
- Tensorflow 2.4.0
- Numpy 1.19.5
the bearing and gear fault datasets are collected from Case Western Reserve University bearing data center, gear vibration dataset of University of Connecticut.
Health states of bearing | Health states of gear |
---|---|
Normal | Chipping 1 (High) |
Inner race 0.007 | Chipping 3 (Middle) |
Inner race 0.0021 | Chipping 5 (Low) |
Outer race 0.007 | Crack |
Outer race 0.014 | Missing |
Outer race 0.021 | Spalling |
Rolling element 0.014 | Healthy |
In this paper, a signal-to-image conversion method [50] is used to transform the original 1D vibration signals into 2D gray images. code: signal-to-image conversion [50] L. Wen, X. Li, L. Gao, Y. Zhang, A new convolutional neural network-based data-driven fault diagnosis method, IEEE Trans. Ind. Electron., 65, pp. 5990-5998, July. 2018. Paper Link: (https://ieeexplore.ieee.org/document/8114247)
Before begin, please separate the training data of different health states into folders in foldergray_images\\
.
The code for training MACGAN and generate gray images istraining and generating.py
, you may also customize the parameters in config part in training and generating.py
. The generated images will be saved in foldergenerated images\\
.
Our SpectralNormalization code is based on (https://github.com/IShengFang/SpectralNormalizationKeras)
If you have any questions about the codes or would like to communicate about intelligent fault diagnosis, fault detection, please contact us: liwei2020@hnu.edu.cn Mentor E-mail:hdshao@hnu.edu.cn