liguge/Deep-Residual-Shrinkage-Networks-for-intelligent-fault-diagnosis-DRSN-

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liguge/Deep-Residual-Shrinkage-Networks-for-intelligent-fault-diagnosis-DRSN-

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Deep Residual Shrinkage Networks for Intelligent Fault Diagnosis（pytorch）深度残差收缩网络应用于故障诊断

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

Views

Journals-of-Prognostics-and-Health-Management

智能故障诊断和寿命预测期刊（Journals of Intelligent Fault Diagnosis and Remaining Useful Life）

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

Deep-discriminative-transfer-learning-network-for-cross-machine-fault-diagnosis

Deep discriminative transfer learning network for cross-machine fault diagnosis

EWSNet

Physics-informed Interpretable Wavelet Weight Initialization and Balanced Dynamic Adaptive Threshold for Intelligent Fault Diagnosis of Rolling Bearings pytorch

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

DLWCB

基于Laplace小波卷积和BiGRU的少量样本故障诊断方法 (Small sample fault diagnosis based on Laplace wavelet convolution and BiGRU)

MDPS_pytorch

A Rolling Bearing Fault Diagnosis Method Using Multi-Sensor Data and Periodic Sampling （pytorch)

FWA-DBN-ELM-for-intelligent-fault-diagnosis

FWA-DBN-ELM fault diagnosis 故障诊断烟花算法优化DBN-ELM的故障诊断

GTFENet_pytorch

GTFE-Net: A Gramian Time Frequency Enhancement CNN for bearing fault diagnosis

Capsule-network-for-fault-diagnosis

Capsule network for fault diagnosis （胶囊网络用于故障诊断）

Maximum-mean-square-discrepancy

Maximum mean square discrepancy: A new discrepancy representation metric for mechanical fault transfer diagnosis

MIXCNN_pytorch

A fault diagnosis method for rotating machinery based on CNN with mixed information

xLSTM-for-intelligent-fault-diagnosis-of-rolling-bearings

xLSTM: Extended Long Short-Term Memory for Intelligent Fault Diagnosis of Rolling Bearings

WIDAN

Interpretable Physics-informed Domain Adaptation Paradigm for Cross-machine Transfer Fault Diagnosis （故障诊断）

liguge

AMSGradP-for-intelligent-fault-diagnosis

Imporved AdamP called AMSGradP is suitable for intelligent fault diagnosis (故障诊断)

PyDSN

Interpretable modulated differentiable STFT and physics-informed balanced spectrum metric for freight train wheelset bearing cross-machine transfer fault diagnosis under speed fluctuations

Variance-discrepancy-representation-pytorch

Variance discrepancy representation: A vibration characteristic-guided distribution alignment metric for fault transfer diagnosis

IDSN_public

IDSN：一种用于高速列车牵引电机跨机诊断的单级可解释可微 STFT 域自适应网络 fault diagnosis

OpenSetMethodForFaultDetection

PGCNN

DS-DAN

Dilated-Causal-Conv1d