gufeicang/dagcn

This code is about the implementation of Domain Adversarial Graph Convolutional Network for Fault Diagnosis Under Variable Working Conditions.

Domain Adversarial Graph Convolutional network (DAGCN)

This code is about the implementation of Domain Adversarial Graph Convolutional Network for Fault Diagnosis Under Variable Working Conditions.

Note

The DAGCN consists of a CNN and a MRF_GCN, and the framework of this code is based on Unsupervised Deep Transfer Learning for Intelligent Fault Diagnosis: An Open Source and Comparative Study.

Implementation

python ./DAGCN/train_advanced.py --model_name DAGCN_features --checkpoint_dir ./DAGCN/results/ --data_name CWRU --data_dir D:/Data/西储大学轴承数据中心网站 --transfer_task [3],[0] --last_batch True

Citation

MRF_GCN: @ARTICLE{MRF_GCN, author={T. {Li} and Z. {Zhao} and C. {Sun} and R. {Yan} and X. {Chen}}, journal={IEEE Transactions on Industrial Electronics}, title={Multi-receptive Field Graph Convolutional Networks for Machine Fault Diagnosis}, year={2020}, volume={}, number={}, pages={1-1}, doi={10.1109/TIE.2020.3040669}}

DAGCN: @ARTICLE{9410617, author={T. {Li} and Z. {Zhao} and C. {Sun} and R. {Yan} and X. {Chen}}, journal={IEEE Transactions on Instrumentation and Measurement}, title={Domain Adversarial Graph Convolutional Network for Fault Diagnosis Under Variable Working Conditions}, year={2021}, volume={70}, number={}, pages={1-10}, doi={10.1109/TIM.2021.3075016}}