DIAGNOSIS FOR WHEELSET OUT-OF-ROUNDNESS OF METRO VEHICLE USING VMD COMBINED WITH OPTIMIZED MCKD

Wheel out-of-roundness (OOR) is a prevalent issue in rail transit vehicles, posing potential safety hazards to electric multiple units (EMUs) and significantly affecting passenger ride comfort. However, current research predominantly focuses on dynamic simulation analyses, with relatively few studies targeting the vibration characteristics associated with wheel OOR. To address this gap, this paper proposes a novel diagnostic method that utilizes Variational Mode Decomposition (VMD) to extract salient signal features and employs the Grey Wolf Optimizer (GWO) to determine the optimal parameters for Maximum Correlated Kurtosis Deconvolution (MCKD) based on minimum sample entropy. Finally, the fault characteristic frequencies are extracted through envelope spectrum analysis. The method was validated on real-world wheel OOR data collected from operational trains. The results demonstrate that the proposed approach effectively isolates the fault characteristic information of wheel OOR, providing a robust basis for further research and practical application in this domain.