Awesome

pytorch-Deep-Residual-Shrinkage-Networks (DRSN)

Reference:

@ARTICLE{8850096,  
author={Zhao, Minghang and Zhong, Shisheng and Fu, Xuyun and Tang, Baoping and Pecht, Michael},  
journal={IEEE Transactions on Industrial Informatics},   
title={Deep Residual Shrinkage Networks for Fault Diagnosis},   
year={2020},  
volume={16},  
number={7},  
pages={4681-4690},  
doi={10.1109/TII.2019.2943898}  
}

Li K. School of mechanical engineering. Jiangnan University; 2019, http://mad-net.org:8765/explore.html?t=0.5831516555847212  [Accessed on August 2019].

Functions:

This is the complete implementation of pytorch version of deep shrinkage residual network. The data set is the bearing data set of Jiangnan University.

Others:

@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}  
}

Environment

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

Awesome

pytorch-Deep-Residual-Shrinkage-Networks (DRSN)

Reference:

Functions:

Others:

Environment

Views