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Developing an Enhanced Short-Range Railroad Track Condition Prediction Model for Optimal Maintenance Scheduling

机译：开发用于优化维护计划的增强型近程铁轨状况预测模型

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摘要

As railroad infrastructure becomes older and older and rail transportation is developing towards higher speed and heavier axle, the risk to safe rail transport and the expenses for railroad maintenance are increasing. The railroad infrastructure deterioration (prediction) model is vital to reducing the risk and the expenses. A short-range track condition prediction method was developed in our previous research on railroad track deterioration analysis. It is intended to provide track maintenance managers with two or three months of track condition in advance to schedule track maintenance activities more smartly. Recent comparison analyses on track geometrical exceptions calculated from track condition measured with track geometry cars and those predicted by the method showed that the method fails to provide reliable condition for some analysis sections. This paper presented the enhancement to the method. One year of track geometry data for the Jiulong-Beijing railroad from track geometry cars was used to conduct error analyses and comparison analyses. Analysis results imply that the enhanced model is robust to make reliable predictions. Our in-process work on applying those predicted conditions for optimal track maintenance scheduling is discussed in brief as well.
机译：随着铁路基础设施越来越老，铁路运输正朝着更高的速度和更重的车轴发展，安全铁路运输的风险和铁路维护的费用也在增加。铁路基础设施恶化（预测）模型对于降低风险和费用至关重要。在我们先前对铁轨劣化分析的研究中，开发了一种短程状态预测方法。旨在为轨道维护经理提前提供两三个月的轨道状况，以更智能地安排轨道维护活动。最近对由轨道几何车测得的轨道状况和该方法预测的轨道状况计算出的轨道几何异常的比较分析表明，该方法无法为某些分析断面提供可靠的条件。本文介绍了该方法的增强功​​能。九龙北京铁路从轨道几何车厢获得的一年的轨道几何数据用于进行误差分析和比较分析。分析结果表明，增强的模型对做出可靠的预测具有鲁棒性。还简要讨论了我们在应用这些预测条件以进行最佳轨道维护计划方面的过程。

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  • 来源
    《Mathematical Problems in Engineering》 |2015年第21期|796171.1-796171.12|共12页
  • 作者

    Xu Peng; Jia Chuanjun; Li Ye; Sun Quanxin; Liu Rengkui;

  • 作者单位

    Beijing Jiaotong Univ, MOE Key Lab Urban Transportat Complex Syst Theory, Beijing 100044, Peoples R China;

    Beijing Jiaotong Univ, MOE Key Lab Urban Transportat Complex Syst Theory, Beijing 100044, Peoples R China;

    Railrd Maintenance Of Way Dept Nanchang, Nanchang 330002, Jiangxi, Peoples R China;

    Beijing Jiaotong Univ, MOE Key Lab Urban Transportat Complex Syst Theory, Beijing 100044, Peoples R China;

    Beijing Jiaotong Univ, MOE Key Lab Urban Transportat Complex Syst Theory, Beijing 100044, Peoples R China;

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