摘要
提出了利用改进经验模态分解的方法来分离轴重信号中的动态轮胎力。在简述轴重信号特点和模态分解步骤的基础上,详细分析了端点效应和虚假模态产生的原因,利用Auto-regressive(AR)模型端点延拓法和相关系数法分别抑制端点效应和判断虚假模态,把虚假模态和残余量加在一起形成新的残余量作为轴重估计。实验结果表明了该方法的有效性,在车速不大于20 km/h时,轴重测量的最大误差为4.34%。
The improved empirical mode decomposition （EMD） is used to separate the dynamic tire forces contained in the axle-weight signal. The characteristics of axle-weight signal and EMD process are introduced, and the causes of the end effect and pseudo-IMF are analyzed in detail. The auto-regressive （AR） model prediction method is employed to extend the data points of axle-weight signal, and the correlation coefficient method is proposed to judge the pseudo-IMFs. The pseudo-IMFs and the residue are added to form a new residue. The new residue is regarded as the axle-weight estimation. The result shows that the method is effective and the maximum weighing error of the axle-weight is 4.34% at the speed of 20 km/h or lower.
出处
《数据采集与处理》
CSCD
北大核心
2008年第6期751-755,共5页
Journal of Data Acquisition and Processing
关键词
动态称重
经验模态分解
端点效应
虚假模态
weigh-in-motion
empirical mode decomposition（EMD）
end effect
pseudo-IMF
