Research on transfer learning diagnosis method for planetary gearbox bearings based on variational embedding composite multiscale diversity entropy and manifold dynamic distribution adaptation

Abstract

To improve the generalisation ability of traditional machine learning fault diagnosis model, this paper proposed a planetary gearbox bearing diagnosis method based on the variational embedded composite multiscale diversity entropy (veCMED) and manifold dynamic distribution adaptation (MDDA). Firstly, fault features are extracted using variational embedded composite multiscale diversity entropy (veCMDE). Secondly, the extracted entropy fault features are combined with manifold dynamic distribution adaptation (MDDA) of the flow shape to map the entropy values of different distributions to the space of the same distribution. Thirdly, the entropy value after feature mapping is input into the KNN classifier for fault identification. The final experimental results show that the proposed veCMDE and MDDA based transfer learning method has the best generalisation ability compared to seven transfer learning methods.