Aiming at the problem that the fault diagnosis accuracy of wind turbine bearings is reduced due to the variable operating conditions of wind turbines in actual wind farms and the lack of data completeness,this paper proposes a wind turbine bearing fault diagnosis method based on Multiple Wide Kernel Convolutional Neural Networks and transfer learning fusion.Firstly,MWKCNN wind turbine bearing fault diagnosis model is trained in source domain.Secondly,according to the similarity between the three target domains and the source domain,the MWKCNN model structure of the source domain is adjusted by the transfer learning method based on model fine-tuning,and verified by the actual bearing data set.The simulation results show that the fault diagnosis accuracy of the MWKCNN model in the source domain reaches 99.48%,and the fault diagnosis accuracy of the wind turbine bearing reaches more than 94%in the three target domains with missing data completeness.Compared with other model migration effects,the MWKCNN model has stronger bearing vibration signal fault feature mining capabilities.
关键词
多重宽核卷积神经网络/风机轴承/故障诊断/迁移学习/变工况数据量缺失/下采样损失
Key words
multiple wide-kernel convolutional neural networks/wind turbines bearings/fault diagnosis/transfer learning/missing data in variable working conditions/downsampling loss
维普
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