A Novel Bearing Fault Diagnosis Method Based on Multi-scale Transfer Sample Entropy

Abstract

To solve the defects that the generalization ability of the traditional data-driven fault diagnosis model reduces or even fails in mechanical system diagnosis, a multi-scale transfer sample entropy is proposed based on the idea of transfer learning. First, the multi-scale transfer sample entropy method is proposed to extract fault features based on multi-scale sample entropy and feature transfer learning. Second, the parameters of the multiscale transfer sample entropy method are optimized to further improve the fault identification accuracy. Finally, the experiment results of rolling bearing show that the proposed multi-scale transfer sample entropy can effectively improve the generalization ability of the data-driven model and realize the accurate identification of different fault locations of the rolling bearing under a small number of samples.