Parameter Identification of Wheel Polygonization Based on Effective Signal Extraction and Inertial Principle

Violent interactions between wheel and rail caused by the polygonal wheels can strongly influence the operational comfort and safety of railway vehicles. It is an effective tool to dynamically detect wheel defects with the axle box acceleration signals. However, since the onboard vibration signals of the axle box are mixed with abundant noise from track irregularity and vehicle components, it is difficult to ensure the detection accuracy of the polygonal wheel. A novel strategy is proposed to extract the effective signal components and quantitatively identify the polygonal wheel parameters. First, the genetic mutation particle swarm optimization (GMPSO) algorithm is developed to search for optimization parameters of variational mode decomposition (VMD). Specifically, the minimum approximate entropy is adopted for the objective function, and the over-decomposition issue of VMD is addressed by the criterion of center frequencies ratio. Then, the independent components (ICs) are separated from VMD modes and the original signal by fast independent component analysis (FastICA). The dominated signal components related to the polygonal wheels can be decided by the proposed criterion. Finally, the order and amplitude of the wheel polygonal wear are obtained from the effective IC based on the inertial principle. The effectiveness of the proposed parameter identification algorithm is verified by the dynamics simulation and field test, and the estimation accuracy of wear amplitudes outperforms the comparison methods.