/1D-Grad-CAM-for-interpretable-intelligent-fault-diagnosis

智能故障诊断中一维类梯度激活映射可视化展示 1D-Grad-CAM for interpretable intelligent fault diagnosis

Primary LanguagePython

Grad-CAM-1D-pytorch (Pytorch)(Keras)

故障诊断（fault diagnosis）

This is the 1D-Grad-CAM implementation of pytorch version of Fault diagnosis for small samples based on attention mechanism

However, in fact, the title Fault diagnosis for small samples based on interpretable improved space-channel attention mechanism and improved regularization algorithms fits the research content of the paper better.

Maybe you need a software called OriginLab to visualize the class activation gradient or Matlibplot.

This model can input one-dimensional vibration signals directly.

Everyone can refer to 1D-Grad-CAM++(Recommendation!), and later we have made a lot of modifications compared to 1D-Grad-CAM.

If this project helps you, welcome to cite it:

@article{ZHANG2022110242,  
title = {Fault diagnosis for small samples based on attention mechanism},  
journal = {Measurement},  
volume = {187},  
pages = {110242},  
year = {2022},  
issn = {0263-2241},  
doi = {https://doi.org/10.1016/j.measurement.2021.110242 },  
url = {https://www.sciencedirect.com/science/article/pii/S0263224121011507},  
author = {Xin Zhang and Chao He and Yanping Lu and Biao Chen and Le Zhu and Li Zhang}  
}

image

Effect

微信图片_20220404222733

微信截图_20220509131105

Reference

https://github.com/agis09/grad_cam_1d/blob/master/grad_cam.py

https://github.com/jacobgil/pytorch-grad-cam

@inproceedings{selvaraju2017grad,
  title={Grad-cam: Visual explanations from deep networks via gradient-based localization},
  author={Selvaraju, Ramprasaath R and Cogswell, Michael and Das, Abhishek and Vedantam, Ramakrishna and Parikh, Devi and Batra, Dhruv},
  booktitle={Proceedings of the IEEE international conference on computer vision},
  pages={618--626},
  year={2017}
}
@inproceedings{chattopadhay2018grad,
  title={Grad-cam++: Generalized gradient-based visual explanations for deep convolutional networks},
  author={Chattopadhay, Aditya and Sarkar, Anirban and Howlader, Prantik and Balasubramanian, Vineeth N},
  booktitle={2018 IEEE winter conference on applications of computer vision (WACV)},
  pages={839--847},
  year={2018},
  organization={IEEE}
}

Others works

@ARTICLE{9374403,  
author={Luo, Hao and He, Chao and Zhou, Jianing and Zhang, Li},  
journal={IEEE Access},   
title={Rolling Bearing Sub-Health Recognition via Extreme Learning Machine Based on Deep Belief Network Optimized by Improved Fireworks},   
year={2021},  
volume={9},  
number={},  
pages={42013-42026},  
doi={10.1109/ACCESS.2021.3064962},  
url = {https://ieeexplore.ieee.org/document/9374403},  
}

Environment

pytorch 1.10.0
python 3.8
cuda 10.2