Fault Diagnosis for Rail Profile Data Using Refined Dispersion Entropy and Dependence Measurements

The diagnosis of railway system faults is significant for its comfort, efficiency, and safety. The rail profile faults are the most direct impact factors when considering the health conditions of rails. This paper puts forward rail fault diagnosis from two perspectives: quantifying the level of complexity and chaos of different profiles, and measuring the level of correlation between different profiles, which correspond to the newly proposed refined dispersion entropy (RDE) method and the correlation plane method, respectively. The RDE uses weighted-dispersion patterns to extract accurate time domain features from rail profile data, and the correlation plane can characterize nonlinear and non-monotonic relationships between analyzing subjects, which are the main contributions of this study. Experimental results with simulated and reality-based data show that the proposed methods can identify faulty profile data and discriminate different types of profile faults more effectively when compared with existing methods.