基于轨道数据对齐的ARIMA模型的轨道不平顺预测

Prediction of Track Irregularities by the ARIMA Model of Aligned Track Data

轨道几何尺寸数据是在对被测轨道进行检查时得到的,而不同时间的历史数据,由于检查环境和条件存在变动,其数据表现经常伴随着累积里程误差的存在,导致数据存在无法对齐的现象,从而不能精准预测轨道不平顺的发展。针对此问题,提出将多组原始数据依次以某一步长进行分段验证,以互相关函数相互进行评价,将各组原始数据的里程对齐之后得到有效的观测值。以广铁集团惠州工务段杭深线潮汕站4道K1317+150-K1317+350间的2013-2015年度的历史数据作为试验样本,通过建立自回归积分滑动平均模型(auto-regressive integrated moving average model,简称ARIMA)预测轨道不平顺。结果表明,将轨道几何尺寸原始数据对齐后再进行其不平顺状态的预测研究,可以达到更高的试验精度,其相对误差绝对值的最大值小于5%,样本中相对误差均值为1.75%,适用于工程。

Track geometry data is obtained by checking the measured track,however,the performance of historical data from different time is often accompanied by the existence of cumulative mileage errors due to changes in inspection environment and conditions,it will lead to a phenomenon of data that cannot be aligned,then it is impossible to predict the development of track irregularities accurately;It is proposed that the multiple sets of raw data should be verified in subsection at a certain step,cross correlation function is used to evaluate each other,the effective observations are obtained after each group’s raw data is aligned;then,the historical data in Guangzhou Railway Group Huizhou Railway Section Hangzhou-Shenzhen Line Chaoshan Railway Station No.4 Road K1317+150-K1317+350 between 2013-2015 as the test sample is used to predict the track irregularities by building the ARIMA model.The result shows:research on the prediction of track irregularity after the raw data of track geometry size has been aligned that can achieve higher test accuracy,the maximum relative error is less than 5%,the average relative error is1.75%in the sample.