摘要
轨检车在铁路线路状态检测和养护维修方面发挥着不可替代的作用.历史检测数据具有可比性是获取可靠线路状态变化趋势的关键基础,因而也是开展线路状态修的基础.影响检测数据可比性的主要因素是里程偏差.既有研究基于区段上各采样点里程偏移为常数,利用相关分析修正里程偏差.然而,现场生产实践和轮轨关系研究表明,里程偏移量是一个随机变量.本文研究建立了轨检车数据采样点最优配对模型,捕捉每一个采样点的里程偏移量,建立的模型被用来修正某线路的17次检测数据,修正后的检测数据展示了模型的可靠性和现场应用可行性.
Track geometry cars play an irreplaceable role in track inspection and maintenance. It is essential for the implementation of condition-based track maintenance to compare historical track geometry data from track geometry cars. Milepoint measurement errors of track geometry data have the leading adverse effect on the comparability of historical data. Researches on milepoint measurement error correction assume that milepoint shifts over a track length are constant. It is well known that milepoint shifts are actually stochastic.The paper presents an Optimal Match model of Sampling Points for track geometry data from track geometry cars to capture milepoint shift for each sampling points. The model is applied to track geometry data from 17 inspection runs. The performance analysis results confirmed the robustness and applicability of the model.
出处
《交通运输系统工程与信息》
EI
CSCD
北大核心
2017年第3期172-177,共6页
Journal of Transportation Systems Engineering and Information Technology
基金
中央高校基本科研业务经费专项资金(T15JB00340)~~
关键词
铁路运输
轨检车
动态规划
里程偏差修正
检测数据
railway transportation
track geometry car
dynamic programming
milepoint error correction
inspection data
