Generalized Gaussian Distribution Refined Composite Multiscale Fluctuation Dispersion Entropy and Its Application in Fault Diagnosis of Switch Machine

Abstract

The switch machine (SM) is an important device for turnout conversion, which is of great significance to ensure the safety of train operations. Refined composite multiscale dispersion entropy (RCMDE) is a formidable nonlinear characterization tool for time series signals, which has been applied to the fault diagnosis (FD) of switch machines. In fact, the lack of nonlinear mapping ability of RCMDE and the inability to evaluate the volatility of the SM signal affect its ability to extract features. To overcome its inherent drawbacks, a generalized Gaussian distribution refined composite multiscale fluctuation dispersion entropy (GGRCMFDE) is proposed to measure the complexity of the SM signal. In GGRCMFDE, first, the nonlinear mapping ability of the algorithm is improved by replacing the normal cumulative distribution function (NCDF) with the generalized Gaussian distribution (GGD). The fluctuation theory is introduced to evaluate the fluctuation of the signal to better adapt to the phenomenon of nonperiodic fluctuation of the signal when the SM fails. Through the above improvement, the feature extraction capability of the algorithm is comprehensively enhanced. Second, an FD method for the SM is used by combining the fault features extracted by GGRCMFDE with the support vector machine (SVM) for fault classification. Finally, the algorithm’s performance is guaranteed by improving dung beetle optimization (IDBO) algorithm, and the superiority of the diagnosis method is improved by using IDBO to optimize SVM; we name this method GGRCMFE–IDBO–SVM. It is verified by the actual operation scene experiment of the switch machines. The experiment shows that compared to the other algorithms, the FD impact of GGRCMFE–IDBO–SVM is significant, and a taller fault identification precision can be obtained.