Rolling Bearing Fault Diagnosis Based on SK-ResNet and Transfer Learning
Aiming at the problems of poor generalization ability and low diagnosis accuracy of traditional deep learning model under variable working conditions,a fault diagnosis method of rolling bearing based on SK-ResNet and transfer learning was proposed.First,the frequency domain signal is obtained by fast Fou-rier transform(FFT),and a new time-frequency domain data set is obtained by weighted fusion.Secondly,the selective kernel network(SKNet)is integrated into the residual network(ResNet)to improve the fea-ture extraction capability.Then,the differential alignment loss(DDM)based on multi-core maximum mean difference(MK-MMD)and correlated alignment(CORAL)is used to reduce the feature distribu-tion difference of rolling bearing fault data under varying working conditions,and it is applied to multiple modules of the model to further reduce the distribution distance between features.The experimental results show that the proposed method has better diagnostic accuracy and generalization ability than the traditional rolling bearing fault diagnosis method.
万方数据
维普
