Research on axle-box bearing fault feature extraction algorithm based on simulation test and BOA-VMD - ZHANG Dongxing; YANG Gang; ZHOU Ao; QIN Limu; WEI Yuqian; YAN Lei (机车电传动)
Aiming at the problem that axle-box bearing faults are difficult to find during the operation of urban rail trains, a bearing fault feature extraction based on variational mode decomposition (VMD) parameter optimization using butterfly optimization algorithm (BOA) was proposed. Firstly, a bearing fault dynamic model based on the rigid-flexible coupling of bearing-vehicle was constructed, and the vibration signal of the axle box under the wheel-rail disturbance and the faulty bearing was extracted. Then, the BOA algorithm is used to optimize the VMD modal component number and the second penalty coefficient of the axle box vibration signal, so as to determine the best parameter combination. Finally, by using the determined optimal parameters, the vibration signal of the bearing was decomposed by VMD to obtain different intrinsic mode components (intrinsic mode function, IMF), and an envelope analysis was performed to find the eigen frequencies of bearing failures. Through the experimental analysis, it can be seen that the VMD analysis method of optimizing parameters can effectively find the characteristic frequency of bearing faults, and by comparing the EMD analysis method, it can be found that the analysis method proposed in this paper is more effective.