基于注意力机制的少量样本故障诊断 pytorch.zip

# DCA-BiGRU The pytorch implementation of the paper [Fault diagnosis for small samples based on attention mechanism](https://doi.org/10.1016/j.measurement.2021.110242) **However, in fact, the title [Fault diagnosis for small samples based on interpretable improved space-channel attention mechanism and improved regularization algorithms](https://doi.org/10.1016/j.measurement.2021.110242) fits the research content of the paper better.** The dataset comes from 12khz, 1hp  # Contributions: 1. **1D-signal attention mechanism** [[code](https://github.com/liguge/Fault-diagnosis-for-small-samples-based-on-attention-mechanism/blob/main/oneD_CS_attention.py)] 2. **AMSGradP** [[code](https://github.com/liguge/AMSGradP-for-intelligent-fault-diagnosis)] 3. **1D-Meta-ACON** [[code](https://github.com/liguge/Fault-diagnosis-for-small-samples-based-on-attention-mechanism/blob/main/oneD_Meta_ACON.py)] 4. **At the beginning, I found that many model designs did not connect GAP operation after BiGRU/BiLSTM, which is the basically routine operation. I found that GAP works very well.** [[code](https://github.com/liguge/Fault-diagnosis-for-small-samples-based-on-attention-mechanism/blob/beb35522b283853aa12390721136583bb09821bf/model_train.py#L119)] 5. **1D-Grad-CAM++** [[code](https://github.com/liguge/1D-Grad-CAM-for-interpretable-intelligent-fault-diagnosis)] 6. **AdaBN** [[code](https://github.com/liguge/Fault-diagnosis-for-small-samples-based-on-attention-mechanism/blob/main/adabn.py)] # Attention Block（SCA）  # How does it work?  # If it is helpful for your research, please kindly cite this work: ```html @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 ```html @article{HE, title = {Physics-informed interpretable wavelet weight initialization and balanced dynamic adaptive threshold for intelligent fault diagnosis of rolling bearings}, journal = {Journal of Manufacturing Systems}, volume = {70}, pages = {579-592}, year = {2023}, issn = {1878-6642}, doi = {https://doi.org/10.1016/j.jmsy.2023.08.014}, author = {Chao He and Hongmei Shi and Jin Si and Jianbo Li} } ``` # Environment pytorch == 1.10.0 python == 3.8 cuda == 10.2 # Contact - **Chao He** - **chaohe#bjtu.edu.cn (please replace # by @)** ## Views