Smoothing Methods of Wheel Out-of-Roundness Signals and Their Effects on Polygonal Wear Prediction

As defects such as pitting usually occur on the wheel tread, the measured wheel out-of-roundness (OOR) signals often contain high-frequency noise interference, and sometimes the signals are discontinuous at the start and end points due to objective factors. Wheel OOR is an important wheel-rail interface excitation in the vehicle-track coupled dynamics model, exerting significant effects on the simulation of dynamic wheel-rail interaction and wheel OOR wear prediction. Selecting the suitable smoothing method is key to ensuring the accuracy of the simulation results. The smoothing effects of four commonly adopted methods based on the EN 15610 standard, Fourier series, moving average, and morphological filtering on processing wheel OOR signals were investigated, and the applicability of the four methods in predicting polygonal wear was discussed. The results indicate that the two methods of Fourier series and moving average can achieve sound smoothing and de-noising effect, preserve the waveform characteristics of original signals, and ensure continuity and differentiability at the start and end points of wheel OOR data when processing measured wheel OOR signals. Additionally, the two methods are also suitable for application in polygonal wear prediction. When the two methods are employed, the order of the Fourier series should be greater than 60 and the smoothing window length of moving average should be about 17 mm.