高速列车隧道压力波数据的分段线性表示  

Piecewise linear representation of pressure wave data of high-speed trains traveling through tunnels

作　　者：夏玉涛 刘堂红 王鑫然 陈争卫 许彬 郭子健 李文辉 XIA Yu-tao;LIU Tang-hong;WANG Xin-ran;CHEN Zheng-wei;XU Bin;GUO Zi-jian;LI Wen-hui(Key Laboratory of Traffic Safety on Track of Ministry of Education,School of Traffic&Transportation Engineering,Central South University,Changsha 410075,China;Joint International Research Laboratory of Key Technology for Rail Traffic Safety,Central South University,Changsha 410075,China;National&Local Joint Engineering Research Centre of Safety Technology for Rail Vehicle,Central South University,Changsha 410075,China;Department of Civil and Environmental Engineering,The Hong Kong Polytechnic University,Hung Hom,Kowloon,Hong Kong 999077,China)

机构地区：[1]Key Laboratory of Traffic Safety on Track of Ministry of Education,School of Traffic&Transportation Engineering,Central South University,Changsha 410075,China [2]Joint International Research Laboratory of Key Technology for Rail Traffic Safety,Central South University,Changsha 410075,China [3]National&Local Joint Engineering Research Centre of Safety Technology for Rail Vehicle,Central South University,Changsha 410075,China [4]Department of Civil and Environmental Engineering,The Hong Kong Polytechnic University,Hung Hom,Kowloon,Hong Kong 999077,China

出　　处：《Journal of Central South University》2023年第7期2411-2426,共16页中南大学学报（英文版）

基　　金：Project(2022RC3040)supported by the Science and Technology Innovation Program of Hunan Province,China;Project(51975591)supported by the National Natural Science Foundation of China;Project(P2021T013)supported by the Technology Research and Development Program of China Railway;Project(202106370111)supported by the China Scholarship Council;Project(CX20210232)supported by the Hunan Provincial Innovation Foundation for Postgraduate,China;Project(2021zzts0163)supported by the Fundamental Research Funds for the Central Universities,China。

摘　　要：分段线性表示(PLR)技术目前已在多个领域被广泛用于重新表征高维的时间序列数据,以实现数据维度降低、波动过滤和整体趋势提取的目的。然而,该技术目前尚未在高速列车的隧道压力波数据的相关分析处理中得到应用。因此,本研究首次引入PLR技术对典型的高维列车压力波数据序列进行重新表征,并针对高速列车的压力波数据专门设计了一种基于感知重要点(PIP)且性能表现较好的PLR算法。研究结果表明,数据点重要性的度量方法和分段误差评估方法,特别是前者,对压力波序列的PIP的识别优先级甚至最终结果都会产生影响。与欧氏距离(ED)和正交距离(OD)相比,当将垂直距离(VD)作为数据点重要性的度量方法时,PLR_PIP算法(PLR_PIP_VD)获得了更合理的高速列车压力波的PLR结果。通过累积误差、平均误差和最大误差三种分段误差评估方法之间的对比,当将累积误差作为分段误差评估方法时,PLR_PIP_VD算法得到了相对较好的高速列车压力波的PLR结果。提出的适用于高速列车压力波PLR分析的PLR_PIP算法,为高速列车的压力波数据序列的分析处理提供了一种新的方法。Piecewise linear representation(PLR)techniques have been widely adopted to recharacterize highdimensionality time series data in numerous fields for purposes of dimensionality reduction,fluctuation filtering,and overall trend extraction.However,this technique has not yet been applied to pressure waves of high-speed trains(HSTs).This study therefore introduced PLR techniques to recharacterize typical high-dimensionality pressure waves for the first time.A well-performing PLR algorithm based on perceptually important points(PIPs)was specifically designed for pressure waves of HSTs.The results reveal that the measurement methods of data point importance and assessment methods for segmentation errors,particularly the former,have impacts on identification priority and even the final result of PIPs of pressure waves.The PLR_PIP algorithm using vertical distance as the measurement of data point importance(PLR_PIP_VD)achieves a more reasonable PLR of pressure waves compared to those of the Euclidean distance and orthogonal distance.Through comparisons among cumulative error,average error,and maximum error,the PLR_PIP_VD algorithm employing cumulative error as the assessment method of segmentation errors accomplished a preferable PLR of pressure wave.The design of the PLR_PIP algorithm applicable to the PLR of pressure waves was finally proposed.This provides a novel processing method for pressure waves of HSTs.

关 键 词：高速列车 隧道 压力波 时间序列 分段线性表示 算法设计 

分 类 号：U451.3[建筑科学—桥梁与隧道工程]