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基于轨道动检数据的轨道板的变形识别及预测

李晨钟 利璐 汪健辉 冯晓云 王青元 黄传岳 王永华 何庆

李晨钟, 利璐, 汪健辉, 冯晓云, 王青元, 黄传岳, 王永华, 何庆. 基于轨道动检数据的轨道板的变形识别及预测[J]. 西南交通大学学报, 2022, 57(2): 306-313. doi: 10.3969/j.issn.0258-2724.20200555
引用本文: 李晨钟, 利璐, 汪健辉, 冯晓云, 王青元, 黄传岳, 王永华, 何庆. 基于轨道动检数据的轨道板的变形识别及预测[J]. 西南交通大学学报, 2022, 57(2): 306-313. doi: 10.3969/j.issn.0258-2724.20200555
LI Chenzhong, LI Lu, WANG Jianhui, FENG Xiaoyun, WANG Qingyuan, HUANG Chuanyue, WANG Yonghua, HE Qing. Deformation Recognition and Prediction of Track Slabs Based on Track Inspection Data[J]. Journal of Southwest Jiaotong University, 2022, 57(2): 306-313. doi: 10.3969/j.issn.0258-2724.20200555
Citation: LI Chenzhong, LI Lu, WANG Jianhui, FENG Xiaoyun, WANG Qingyuan, HUANG Chuanyue, WANG Yonghua, HE Qing. Deformation Recognition and Prediction of Track Slabs Based on Track Inspection Data[J]. Journal of Southwest Jiaotong University, 2022, 57(2): 306-313. doi: 10.3969/j.issn.0258-2724.20200555

基于轨道动检数据的轨道板的变形识别及预测

doi: 10.3969/j.issn.0258-2724.20200555
基金项目: 国家自然科学基金(51878576);国家自然科学基金-高铁联合基金重点项目(U1934214)
详细信息
    作者简介:

    李晨钟(1995—),男,博士研究生,研究方向为铁路大数据安全运维,E-mail:lcz1844697176@163.com

    通讯作者:

    何庆(1982—),男,教授,博士,研究方向为铁路大数据安全运维,E-mail:9751749@qq.com

  • 中图分类号: U216.3

Deformation Recognition and Prediction of Track Slabs Based on Track Inspection Data

  • 摘要:

    现有对高速铁路板式无砟轨道变形病害的检测效率不足,检测成本过高,而通过轨道动检数据能够一定程度上反映轨道板变形程度. 因此,搜集了CRTS Ⅰ、Ⅱ、Ⅲ型板线路3 a内的动检数据,引入小波能量作为轨道板变形评价指标,通过建立时空数据挖掘模型实现了不同轨道板的变形定位识别和劣化预测. 研究结果表明:受当地气温影响,轨道板变形程度具有一定的季节性规律,Ⅰ、Ⅱ型板在高温环境下出现翘曲或上拱,Ⅲ型板在低温环境下出现冻胀;3种轨道板中Ⅰ型板变形程度最小,Ⅱ型板最大,Ⅱ型板的残余变形会随时间累积,最终导致高低不平顺超限;长短期记忆网络能够实现对轨道板变形指标15~30 d内的短中期预测,Ⅰ型板变形的最佳预测结果R-square值接近0.9,而Ⅱ型板、Ⅲ型板变形的最佳预测R-square值均超过0.9.

     

  • 图 1  不同轨道板的变形特征波长统计结果

    Figure 1.  Statistical results of deformation characteristic wavelength for different track slabs

    图 2  CRTS Ⅰ 型板部分区段计算结果

    Figure 2.  Calculation results of some sections of CRTS Ⅰ slab

    图 3  虚拟高低不平顺识别结果

    Figure 3.  Identification results of virtual surface irregularity

    图 4  LSTM神经网络结构

    Figure 4.  Structure of LSTM neural network

    图 5  不同轨道板变形预测结果

    Figure 5.  Deformation prediction results of different track slabs

    图 6  温度与各类轨道板变形相关性分析

    Figure 6.  Correlation analysis between temperature and deformation of different track slabs

    图 7  不同轨道板TIDI对数值

    Figure 7.  Logarithmic values of TIDI for different track slabs

    表  1  工况设置参数

    Table  1.   Parameters of operation condition

    参数CRTS Ⅰ
    型板
    CRTS Ⅱ
    型板
    CRTS Ⅲ
    型板
    结构跨数/个202020
    结构长度/mm485664505600
    异常位置/m124~172329~393554~610
    正常幅值(最大
    值)/mm
    0.20.20.2
    异常幅值(最大
    值)/mm
    0.60.60.6
    下载: 导出CSV

    表  2  不同轨道板预测结果的R-square值

    Table  2.   R-square values of prediction results for different track slabs

    历史数据时间长度/dCRTS Ⅰ型板CRTS Ⅱ型板CRTS Ⅲ型板
    15 d30 d45 d15 d30 d45 d15 d30 d45 d
    15 0.85 0.66 0.61 0.92 0.81 0.76 0.92 0.84 0.64
    30 0.88 0.75 0.57 0.91 0.82 0.74 0.93 0.85 0.65
    45 0.88 0.75 0.67 0.91 0.80 0.76 0.93 0.86 0.74
    下载: 导出CSV
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出版历程
  • 收稿日期:  2020-08-18
  • 修回日期:  2020-12-30
  • 网络出版日期:  2022-07-07
  • 刊出日期:  2021-03-02

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