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Railroad Track Deterioration Characteristics Based Track Measurement Data Mining

机译:基于铁轨劣化特性的铁轨测量数据挖掘

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摘要

Accurate information on future railroad track condition is essential to optimally schedule track Maintenance & Renewal activities in order to minimize influences of the activities on rail traffic under constraints of limited budgets and maintaining allowable condition tracks. In this paper, a track measurement data mining method is presented to this aim. It is developed on the basis of track deterioration characteristics. Actual track measurement data is used to analyze errors in track condition predictions by the method. The analysis results show that the proposed method can mine accurate track deterioration rates from historical track measurement data and thus accurately provides future track condition two or three months in advance.
机译:有关未来铁路轨道状况的准确信息对于优化计划轨道维护与更新活动至关重要,以便在预算有限和维持允许的状况轨道下将活动对铁路交通的影响降至最低。为此,本文提出了一种轨道测量数据挖掘方法。它是根据磁道劣化特性开发的。该方法使用实际的轨道测量数据来分析轨道状况预测中的误差。分析结果表明,该方法可以从历史轨道测量数据中准确地得出轨道劣化率,从而提前两三个月准确地提供未来的轨道状况。

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  • 来源
    《Mathematical Problems in Engineering》 |2013年第13期|970573.1-970573.7|共7页
  • 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.;

    Shanghai Bur China Railways, Shanghai 200070, 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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