轨道几何不平顺检测数据里程偏差精细化校正方法

Precision Correction Method for Mileage Deviation of Track Geometric Irregularity Detection Data

为了更好地分析轨道几何不平顺的长期演变规律进而评估轨道结构状态,针对轨道几何不平顺相对里程偏差,在现有校正技术的基础上提出了一种多尺度精细化校正方法。首先利用灰色关联分析法从大尺度层面对轨道几何不平顺数据的整体相对里程偏差进行估计,完成粗略校正;随后将灰色关联分析法与窗口法相结合,构建移动窗口,利用移动窗口从小尺度层面对基准数据和待校正数据完成遍历扫描,进而对整段轨道几何不平顺中每个数据点的局部相对里程偏差进行估计,完成精细化校正。利用一组重载铁路轨道高低不平顺实测数据开展了里程校正测试,对本文方法进行性能评估,并讨论了移动窗口宽度对校正性能的影响,确定了最佳的窗口宽度。结果表明:本文方法可有效消除轨道几何不平顺数据中的整体相对里程偏差和局部相对里程偏差,具有较好的校正效果;窗口宽度过大或过小均会导致不良结果,窗口宽度宜取100 m或125 m。

In order to better analyze the long-term evolution of track geometric irregularities and evaluate the status of track structures,a multi-scale refined correction method was proposed based on existing correction techniques to address the relative mileage deviation of track geometric irregularities.Firstly,the grey correlation analysis method was used to estimate the overall relative mileage deviation of uneven data from a large-scale perspective,and rough correction was completed.Subsequently,the grey correlation analysis method was combined with the window method to construct a moving window.The moving window was used to traverse and scan the benchmark data and the data to be corrected from a small scale layer,and then estimate the local relative mileage deviation of each data point in the entire track geometric irregularities,completing fine correction.A mileage correction test was conducted using a set of measured data on the heavy duty railway track geometric irregularities to evaluate the performance of the method proposed in this paper.The impact of moving window width on the correction performance was discussed,and the optimal window width was determined.The results show that the method proposed in this paper can effectively eliminate the overall relative mileage deviation and local relative mileage deviation in uneven data,and has a good correction effect.A window width that is too large or too small can lead to poor results,and the suitable window width is 100 m or 125 m.