In actual industrial production,the fault data of rolling bearings are sparse,and the small sample data set is easy to lead to over-fitting of the model,so it is necessary to enhance the data to form a training domain.The working conditions of mechanical equipment will change in real time,which leads to the difference between training data and test data,and the accuracy of bearing fault diagnosis is not high.Therefore,an adaptive migration learning method in depth domain based on generating countermeasure network and multi-core maximum mean difference(MK-MMD)is proposed.In this paper,a large number of labeled simulation fault data were obtained by finite element simulation,and a fault diagnosis model with adaptive transfer learning in depth domain was constructed,and the bearing fault diagnosis model was tested by using Case Western Reserve University's bearing data set.In addition,compared with the fault diagnosis results of convolutional neural network(CNN),depth adaptive network(DAN)and depth domain adaptive network(DANN),it was proved that the bearing fault diagnosis method based on data enhancement and migration learning of finite element simulation can effectively improve the accuracy of bearing fault diagnosis.
关键词
滚动轴承/迁移学习/深度域适应/数据增强/有限元模拟
Key words
rolling bearing/transfer learning/adaptation in depth domain/data enhancement/finite element simulation
维普
万方数据