Identification Method for Railway Rail Corrugation Utilizing CEEMDAN-PE-SPWVD.

Rail corrugation intensifies wheel-rail vibrations, often leading to damage in vehicle-track system components within affected sections. This paper proposes a novel method for identifying rail corrugation, which combines Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN), permutation entropy (PE), and Smoothed Pseudo Wigner-Ville Distribution (SPWVD). Initially, vertical acceleration data from the axle box are decomposed using CEEMDAN to extract intrinsic mode functions (IMFs) with distinct frequencies. PE is used to evaluate the randomness of each IMF component, discarding those with high permutation entropy values. Subsequently, correlation analysis is performed on the retained IMFs to identify the component most strongly correlated with the original signal. The selected component is subjected to SPWVD time-frequency analysis to identify the location and wavelength of the corrugation occurrence. Filtering is applied to the IMF based on the frequency concentration observed in the time-frequency analysis results. Then, frequency-domain integration is performed to estimate the rail's corrugation depth. Finally, the algorithm is validated and analyzed using both simulated data and measured data. Validation results show that this approach reliably identifies the wavelength and depth characteristics of rail corrugation. Additionally, the time-frequency analysis results reveal variations in the severity of corrugation damage at different locations.