Refined Composite Multivariate Multiscale Dispersion Entropy and Its Application to Fault Diagnosis of Rolling Bearing

Many nonlinear dynamic and statistic methods, including multiscale sample entropy (MSE) and multiscale fuzzy entropy (MFE), have been widely studied and employed to fault diagnosis of the rolling bearing. Multiscale dispersion entropy (MDE) is a powerful tool for complexity measure of time series, and compared with MSE and MFE, it gets much better performance and costs less time for computation. Since single-channel time series analysis will cause information missing, inspired by multivariate multiscale sample entropy (MMSE) and multivariate multiscale fuzzy entropy (MMFE), refined composite multivariate multiscale dispersion entropy (RCMMDE) was proposed in this paper. After that, RCMMDE was compared with MDE, MMSE, and MMFE by analyzing synthetic signals and the results show that the RCMMDE has certain advantages in terms of robustness. A hybrid fault diagnostics approach is proposed for rolling bearing with a combination of RCMMDE, multi-cluster feature selection, and support vector machine. Also, the proposed method is compared with MDE, MMSE, and MMFE, as well as multivariate multiscale dispersion entropy-based fault diagnosis methods by analyzing the experimental data of rolling bearing, and the result shows that the proposed method gets a higher identification rate than the existing other fault diagnosis methods.