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 it is helpful for your research, please kindly cite this work:

@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}  
} 

Algorithm

Effect

Note

If you need drawing code procedure in python, you can send an e-mail to 22110435 # bjtu.edu.cn ( please replace # by @ ) by school e-mail, I will send it to you by e-mail.

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}
}

Environment

pytorch == 1.10.0
python == 3.8
cuda == 10.2

Contact

• Chao He
• chaohe#bjtu.edu.cn (please replace # by @)

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