基于参数优化VMD和SPWVD的轨道波磨辨识方法

为了有效检测轨道波磨故障,提出一种基于参数优化变分模态分解(VMD,Variable Mode Decomposition)和平滑伪维格纳分布(SPWVD,Smooth Pseudo Wigner Ville Distribution)的轨道波磨辨识方法。采用变步长最小均方(VSSLMS,Variable Step Size Least Mean Square)算法对列车轴箱振动加速度原始信号滤波;对滤波后的信号进行变分模态分解,将分解信号包络熵作为轨道波磨辨识的指标;采用平滑伪维格纳分布对分解后的信号进行时频分析,确定波磨发生的位置及波长;通过仿真信号与实例验证方法的有效性。验证结果表明,该方法可提高轨道波磨辨识的准确性,辅助轨道维修和养护。

Rail corrugation identification method based on parameter optimization VMD and SPWVD

In order to detect the rail corrugation fault effectively, this paper proposed a method of rail corrugation identification based on parameter optimization Variational Mode Decomposition (VMD) and Smooth Pseudo Wigner Ville Distribution (SPWVD). The paper used the VSSLMS (Variable Step Size Least Mean Square) algorithm to filter the original acceleration signal of train axle box vibration, decomposed the filtered signal into variational mode, took the envelope entropy of the decomposed signal as the index of the identification of the rail corrugation, used the SPWVD to carry out the time-frequency analysis of decomposed signal, and determine the location and wavelength of the corrugation. The effectiveness of the method was verified by simulation signals and examples. The results show that the method can be used to improve the accuracy of rail corrugation identification and assist rail maintenance.