BEARING FAULT DIAGNOSIS METHOD FOR WIND TURBINE CONSIDERING INSUFFICIENT DATA AND BASED ON COOPERATIVE GAME MODEL FUSION
Auxiliary classifier generative adversarial network(ACGAN)based on particle swarm optimization(PSO)was proposed to solve the problem of insufficient fault data caused by high cost of wind turbine bearing fatigue experiments.The parameters of ACGAN were optimized by PSO,and then ACGAN was used to generate new samples that were highly similar to the original samples.In view of the low accuracy of a single model for wind turbine bearing fault diagnosis,the cooperative game theory was introduced to fuse the diagnostic results of multiple sub-models,and the diagnostic probability matrix of each sub-model was fused by the cooperative game theory and the final diagnostic results were output.Experimental results show that the optimized ACGAN model and the model fusion based on cooperative game can effectively improve the accuracy of bearing fault diagnosis.
wind turbinesbearingsgenerative adversarial networkfault diagnosismodel fusioncooperative game
NETL
NSTL
万方数据
维普
