基于轨道静检的动检里程偏差修正算法研究

Research on Dynamic Detection Data Mileage Deviation Correction Algorithm Based on Track Static Detection Data

轨道几何状态动态检测时由于光电编码器的动态误差,车轮空转打滑,检测人员置入检测系统的误差等因素的影响,检测数据不可避免地产生里程偏差,导致波形错位,直接使用存在里程偏差的数据时将会影响轨道质量状态的评估精度及线路状态恶化趋势分析的可靠性,无法确保线路现场养护维修效果。提出一种以静检数据为基准的动检数据里程偏差修正方法,并提出主点迭代里程偏差修正PPIC算法,通过模拟数据和高速铁路实测数据,使用PPIC算法和现有最稳定的区段相似波形匹配SSWM算法进行比较,实验结果显示PPIC算法修正后动静检波形吻合度,轨向高低不平顺值较差均值、不确定度及相关系数均有显著提升,且修正效果优于SSWM算法,PPIC算法里程偏差修正精度优于一个静检检测间隔(0.125 m),可有效地对动检数据里程偏差进行纠正。

During dynamic detection of track geometry state,the dynamic errors of photoelectric encoder,wheel spin and slip and the errors written into the detection system by the operator will inevitably cause mileage deviations in the detection data,resulting in waveform misalignment.Direct use of the data with mileage deviations will affect the evaluation accuracy of track quality state and the reliability of line status deterioration trend analysis,unable to ensure the effect of on-site maintenance of the line.In this paper,a dynamic detection data mileage deviation correction method was proposed based on static detection data,along with a principal point iteration correction(PPIC)algorithm.In the experiment,the PPIC algorithm and the existing most stable segment similar waveform matching(SSWM)algorithm were used to process simulated data and measured data of high-speed railway.The results show that,the PPIC algorithm,after correction,significantly improves the consistency of the dynamic and static detection waveforms,the mean difference and measurement uncertainty of dynamic and static detection irregularity,as well as the correlation coefficient of dynamic and static detection data.The correction effect of the PPIC algorithm is better than that of SSWM.The mileage deviation correction accuracy of the PPIC algorithm is better than a static detection interval(0.125 m),which can effectively correct the mileage deviation of dynamic detection data.