Multiscale Distribution Entropy and t-Distributed Stochastic Neighbor Embedding-Based Fault Diagnosis of Rolling Bearings

As a nonlinear dynamic method for complexity measurement of time series, multiscale entropy (MSE) has been successfully applied to fault diagnosis of rolling bearings. However, the MSE algorithm is sensitive to the predetermined parameters and depends heavily on the length of the time series and MSE may yield an inaccurate estimation of entropy or undefined entropy when the length of time series is too short. To improve the robustness of complexity measurement for short time series, a novel nonlinear parameter named multiscale distribution entropy (MDE) was proposed and employed to extract the nonlinear complexity features from vibration signals of rolling bearing in this paper. Combining with t-distributed stochastic neighbor embedding (t-SNE) for feature dimension reduction and Kriging-variable predictive models based class discrimination (KVPMCD) for automatic identification, a new intelligent fault diagnosis method for rolling bearings was proposed. Finally, the proposed approach was applied to analyze the experimental data of rolling bearings and the results indicated that the proposed method could distinguish the different fault categories of rolling bearings effectively.