/Fault-diagnosis-for-small-samples-based-on-attention-mechanism

基于注意力机制的少量样本故障诊断 pytorch

Primary LanguagePython

DCA-BiGRU

The pytorch implementation of the paper 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.

微信图片_20211204105938

Attention Block（SCA）

1-s2 0-S0263224121011507-gr5_lrg

How does it work?

微信图片_20220422112054

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}  
}

Our other 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