Research on Rolling Bearing Fault Diagnosis Based on CEEMD and Transfer Learning
[Purposes]In the actual production environment,due to the complexity of machine features and the change of working conditions,the intelligent diagnosis model needs repeated training when mi-grating across units,which not only increases the time cost,but also increases the consumption of com-puting resources.In order to solve these problems,it is necessary to develop a bearing fault diagnosis method that can adapt to complex machine features and maintain high accuracy under different working conditions.At the same time,the repeated training required for model migration is reduced to achieve more efficient fault identification and prediction.[Methods]The study proposes a rolling bearing fault di-agnosis method based on CEEMD and transfer learning.First,the CEEMD decomposition method is used to decompose the original signal and the kurtosis value of the corresponding component is calculated.Then,the multi-core maximum mean difference method is used for the source domain data and the target domain data.Domain adaptation processing,and finally a migration fault diagnosis test and comparative analysis between the Case Western Reserve University dataset and the American Society for Mechanical Failure Prevention Technology dataset.[Findings]The research results show that compared with the ex-isting direct transfer model algorithm,the improved transfer learning network based on CEEMD has a better transfer effect on different data sets,and its fault diagnosis accuracy is the highest.[Conclusions]It is verified by experiments that the method proposed in the study shows good cross-unit adaptability un-der variable working conditions,and has high fault diagnosis accuracy,which provides a valuable refer-ence for studying similar fault diagnosis scenarios of multiple units under complex working conditions.
rolling bearingCEEMDtransfer learningfault diagnosis
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