BEARING FAULT DIAGNOSIS METHOD FOR WIND TURBINE CONSIDERING INSUFFICIENT DATA AND BASED ON COOPERATIVE GAME MODEL FUSION

被引：0
作者：
Li J. [1 ]
Hu X. [1 ]
Wang L. [2 ]
Ma Y. [1 ]
He Y. [3 ]
机构：
[1] Department of Electric Power Engineering, North China Electric Power University, Baoding
[2] China Three Gorges Corporation, Wuhan
[3] Department of Mechanical Engineering, North China Electric Power University, Baoding
来源：
Taiyangneng Xuebao/Acta Energiae Solaris Sinica | 2024年 / 45卷 / 01期
关键词：
bearings; cooperative game; fault diagnosis; generative adversarial network; model fusion; wind turbines;
D O I：
10.19912/j.0254-0096.tynxb.2022-1489
中图分类号：
学科分类号：
摘要：
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. © 2024 Science Press. All rights reserved.
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页码：234 / 241
页数：7
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