引用本文: 梅文胜, 吕世望, 于安斌, 张鹏, 王涛. 一种地铁轨道相对变形检测与定位方法[J]. 武汉大学学报 ( 信息科学版), 2022, 47(1): 104-110.
MEI Wensheng, LÜ Shiwang, YU Anbin, ZHANG Peng, WANG Tao. Detecting and Locating Method for Subway Track Relative Deformation[J]. Geomatics and Information Science of Wuhan University, 2022, 47(1): 104-110.
 Citation: MEI Wensheng, LÜ Shiwang, YU Anbin, ZHANG Peng, WANG Tao. Detecting and Locating Method for Subway Track Relative Deformation[J]. Geomatics and Information Science of Wuhan University, 2022, 47(1): 104-110.

Detecting and Locating Method for Subway Track Relative Deformation

• 摘要: 利用三维激光扫描所得点云数据识别并提取轨道高程，引入轨道不平顺的研究方法，定义了用于描述不同期之间轨道局部相对位置关系的线状结构局部相对变形波动性指标，提出了基于小波分解和平滑伪维格纳-威尔分布的异常局部相对变形提取方法。小波分解具有放大轨道相对变形特征和缩小频段范围的作用，可确定异常局部相对变形的波长。维格纳-威尔分布能够将信号能量在时-频两域中展开，可分析信号在特定频率下能量的分布，从而确定异常局部相对变形的位置。使用模拟数据验证算法的可行性，并进行了实例分析，结果表明，运用小波分解和维格纳-威尔分布处理地铁隧道三维激光扫描点云中提取的轨道数据，能够从时-频两域分析轨道变形的能量分布，有效地提取出轨道相对变形的相关信息，为轨道相对变形检测提供了一种新的方法。

Abstract:
Objectives  Nowadays, most 3D laser scanning in subway tracks is used to survey the tracks' diameter convergence so as to determine whether section deformation takes place between different periods. However, no measurement has been conducted on the subway track deformation with this method.
Methods  Therefore, using wavelet analysis and Wigner-Ville distribution, this paper carries out the locating research on subway track relative deformation based on point cloud data. The first step is data preprocessing. On the basis of the subway point cloud data acquired by 3D laser scanners, sections are intercepted continuously in equal intervals, and track traits are recognized in section point cloud for track elevation extraction. Then, we define the partial relative deformation fluctuation indexes of the linear structure to describe the relative spatial relationships of partial tracks between different stages. Last but not least, this paper presents an extraction method for abnormal partial relative deformation based on wavelet analysis and smoothed pseudo Wigner-Ville distribution (SPWVD). First, we need to choose a suitable basic function and decomposition level, decompose relative deformation fluctuation by the wavelet method, and calculate the characteristic wavelength based on the fast Fourier transform of signals in different levels. Second, the SPWVD value of decomposed-then-reconstructed signals and the energy corresponding to different mileages in the characteristic wavelength should be determined, and the energy threshold should be set based on the 3σ criterion to locate the relative deformation wave. Third, the range of relative deformations should be settled in relative deformation fluctuations.
Results  This paper verifies the feasibility of this algorithm using simulated data and concrete example analysis. Through the calculation of simulated data, the waveform detected by this method has an approximate location and range with the preset value. By the concrete example analysis, two results are obtained. After the comparison of the design value with the concrete scanning value of the same track, relevant information of relative deformation in characteristic wavelengths is successfully detected. With the scanning point cloud data in two periods that have short time intervals, relative deformation in any characteristic wavelength can hardly be detected.
Conclusions  The calculation results prove the feasibility of this algorithm, show the energy distribution of track relative deformation in both time and frequency domains, and effectively extract the relevant information of track relative deformations, providing a new method to monitor the track relative deformation.

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