Compound Fault Diagnosis Method Guided by Variational Mode Decomposition for Wheelsets and Bearings

A multi-fault feature extraction and matching method guided by variational mode decomposition (VMD) was proposed to address the difficulty in identifying and diagnosing composite faults in train wheelset bearing systems. Firstly, in order to avoid the pre-defined mode number relying on prior knowledge during operation and thus affecting the diagnosis results, the original axle-box vibration data are directly decomposed by VMD step by step, and the number of modes is 2–N. Secondly, the VMD intrinsic mode functions (VIMF) obtained by VMD are calculated to extract the VIMF with the largest correlation kurtosis; then, the determined VIMF is analyzed by square envelope analysis to extract the fault feature frequency. Finally, the proposed method is compared with the fast spectral Kurtogram method and the correlation Kurtogram method. The analysis of simulation signals and experimental data shows that the proposed method can completely avoids the problem of selecting the key parameter K in the VMD model, and can accurately and effectively extract the fault characteristics of wheelsets and bearings, respectively. Compared with the fast spectral Kurtogram method and the correlation Kurtogram method the proposed method can diagnose compound faults effectively, and the obtained fault feature harmonic components are more advantageous in quantity and signal-to-noise ratio.