一种消除高速列车振动信号局部强干扰的方法

摘要

In the train state monitoring experiment, environment influence always caused strong local disturbance in some vibration signals and thus caused false results in the vehicle dynamic performance assessment. In order to solve this problem, a method to eliminate local strong disturbance based on Improved Extremum field Mean Mode Decomposition （IEMMD） was presented. In this method, the extremum points envelopment mean were replaced by an improved extremum field mean and the boundary wave matching algorithm was used to restrain the end effect, the original signal was decomposed into intrinsic mode functions （IMFs） of different time scale and residue, all data in disturbance sections of residue were replaced by zeros, and then all the IMFs and the new residue were superposed to reconstruct the de-noised signal. The analysis results of measured vibration signals of train bogie under strong disturbance show the feasibility and validity of this method.%在高速列车状态检测试验中，外界环境常常会在某些传感器采集的振动信号中形成局部强干扰，从而导致错误的车辆动力学性能评估结果。为解决这一问题，提出一种基于改进经验模态分解消除信号中强干扰的方法。该方法以改进的极值域均值代替极值点包络线的均值来提高局部均值的求解精度，以边界波形匹配预测法来抑制端点效应，将信号分解成不同时间尺度的本征函数（IMFs）和残余项，然后将残余项的强干扰区段幅值设为零，最后将所有IMF与修正的残余项叠加。即得到消除强干扰后的信号。对高速列车转向架轴箱弹簧筒实测强干扰信号进行计算分析的结果表明：该方法在高速列车横向稳定性评价中的可行、有效。