基于HHT的汽车动态称重数据分析方法

Data Analysis for Vehicle Weigh-in-Motion Based on HHT

将希尔伯特-黄变换(H ilbert-huang transform,HHT)引入到汽车动态称重数据的分析中,针对动态称重信号的特点运用了极值延拓法来抑制边界效应,提出了利用经验模分解(Em p irica l m ode decom pos ition,EM D)剩余分量的平均值求汽车动态称重真实轴重的方法,并将该方法与滤波法进行比较;进一步求出信号的H ilbert边际谱,将其与傅里叶幅值谱进行比较。实验结果表明H ilbert边际谱准确地反映了汽车通过称重台时振动系统的频率分布情况,汽车自振频率由此求出;当汽车通过台板的时间大于车辆振动的基频周期时,用该方法处理汽车动态称重信号能得到较理想的结果,车速≤15 km/h时最大误差为5.63%。

The Hilbert-huang transform（HHT） is used to analyze the signal of vehicle weigh- in-motion. The extrema extending method is adopted to restrain the end effects of HHT according to the feature of the signal. A new method for obtaining the real axis weight from dynamic weighting using the residue of empirical mode decomposition （EMD） is presented compared with the filter method. Further, Hilbert marginal spectrum is obtained compared with the Fourier amplitude spectrum and the natural frequency of vehicles is gained from its peak frequency. Results show that Hilbert marginal spectrum reflects the frequency distribution of the whole system exactly while the vehicle passes through the bedplate. High precision of measurement can be achieved as long as the period for which the vehicle passes across the bedplate is longer than the base cycle of vibration. The maximum error is 5.63% when the speed is less than 15 km/h.