EDM在汽车制动力数据平滑处理中的应用

Application of empirical mode decomposition(EMD) in data smoothing for vehicle braking

为了实现对汽车制动力变化的全过程检测,提高系统的检测精度和重复性,提出了将经验模态分解(EMD)方法用于汽车制动力数据的平滑处理中,并与传统的汽车制动力数据平滑处理方法的最小二乘法和样条函数法进行了仿真与实测对比试验。试验结果表明:EMD方法既能够削弱干扰的影响,提高测量曲线的光滑度,使汽车制动力测量的最大偏差从原来的0.67%降到0.16%,又能够保持原有曲线的变化特征,从而保证了系统的检测精度和检测数据的重复性。

In order to achieve the detection of variation of vehicle brake in whole course, and improve system precision and repetition, EMD is applied to data smoothing of vehicle braking. The proposed method is compared with traditional methods, such as least square method and spline function method, by simulation and actual measurement. The result shows that the proposed method can suppress disturbance and improve smoothing of measurement curve. The maximum deviation measurement of vehicle brake can be reduced from 0.67% to 0. 16%, and the characteristics of initial curve can be maintained, so the detection precision and reuse of detection data can be ensured. 2 tabs, 3 figs, 10 refs.