| 124 | 0 | 111 |
| 下载次数 | 被引频次 | 阅读次数 |
基于惯性基准法，以道岔区轨道几何动态检测为研究对象，对轨道几何数据的重复性进行分析对比，进而提出了基于条件阈值的道岔曲股轨向滑动匹配处理算法，并利用现场数据进行验证。结果表明：基于惯性基准法的道岔区轨道几何动态检测数据重复性差值的第95百分位数小于0.4 mm，惯性基准法可以满足正向、侧向过岔的轨道几何动态检测要求，但道岔曲股轨向的动态检测输出包括道岔导曲部分的小半径曲线、道岔尖心轨的特殊结构造成的结构不平顺和真实轨道几何病害。本文提出的基于条件阈值判断的滑动匹配道岔区轨向大值处理算法，可消除道岔曲股轨向的结构不平顺对线路轨道质量指数的影响，输出道岔的轨道几何实测值与道岔轨道几何设计值之间的差异，真实反映道岔区轨道几何不平顺，符合现有的轨道单点大值超限和区段数据统计的评价方法。
Abstract：Based on the inertial reference method, the dynamic detection of track geometry in the turnout area was taken as the research object. The repeatability of track geometry data was analyzed and compared, and a conditional threshold-based algorithm for matching the curved track sliding direction of the turnout was proposed, which is validated by using on-site data. The results show that the 95 th percentile of the repeatability difference of the track geometry dynamic detection data in the turnout area based on the inertia reference method is less than 0.4 mm. The inertial reference method can meet the requirements of forward and lateral turnout area track geometry dynamic detection. However, the dynamic detection output of the curved track direction of the turnout includes small radius curves of the turnout guide curve, structural irregularities caused by the special structure of the turnout point rail, and real track geometry defects. The sliding matching turnout area track direction large value processing algorithm based on conditional threshold judgment proposed in this article can eliminate the influence of the structural irregularity of the curved track direction of the turnout on the track quality index of the line, output the difference between the measured track geometry value of the turnout and the designed track geometry value of the turnout, and truly reflect the track geometry irregularity of the turnout area, which is consistent with the existing evaluation methods of single point large value limit and section data statistics of the track.
[1]ZUO Y,LUNDBERG J,CHANDRAN P,et al.Squat Detection and Estimation for Railway Switches and Crossings Utilising Unsupervised Machine Learning[J].Applied Sciences,2023,13(9):5376.
[2]CHEN C,LI X Q,HUANG K,et al. A Convolutional Autoencoder Based Fault Detection Method for Metro Railway Turnout[J].Computer Modeling in Engineering&Sciences,2023,136(1):471-485.
[3]LI M Y,HEI X H,JI W J,et al.A Fault-diagnosis Method for Railway Turnout Systems Based on Improved Autoencoder and Data Augmentation[J].Sensors,2022,22(23):9438.
[4]EUGÈNE N ,BLASIUS B,WOLFGANG P, et al.The Influence of the Lateral Contact Point Trajectory and the Rotation of the Monoblock on the Impact Loads in Railway Turnouts[J].Journal of Sound and Vibration,2022,536:117118.
[5]OU D X,JI Y Q,ZHANG R, et al.An Online Classification Method for Fault Diagnosis of Railway Turnouts[J].Sensors,2020,20(16):4627.
[6]全顺喜.高速道岔几何不平顺动力分析及其控制方法研究[D].成都：西南交通大学，2012.QUAN Shunxi. Dynamic Analysis and Control Method of Geometric Irregularity of High-speed Turnout[D]. Chengdu:Southwest Jiaotong University,2012.
[7]陈龙.高速铁路道岔精测及数据处理方法研究[D].成都：西南交通大学，2014.CHEN Long. Research on High-speed Railway Turnout Precision Measurement and Data Processing Method[D].Chengdu:Southwest Jiaotong University, 2014.
[8]霍海龙.高速铁路道岔区轨道不平顺及钢轨磨耗的多尺度分析[D].北京：北京交通大学，2021.HUO Hailong. Multi-scale Analysis of Track Irregularity and Rail Wear in Turnout Area of High-speed[D].Beijing:Beijing Jiaotong University, 2021.
[9]杨友涛.高速铁路轨道动态检测数据时频特征挖掘及平顺性评价模型与方法[D].成都：西南交通大学，2021.YANG Youtao.Time-frequency Feature Mining of High-speed Railway Track Dynamic Detection Data and Ride Comfort Evaluation Model and Method[D]. Chengdu:Southwest Jiaotong University,2021.
[10]秦航远.基于多源检测数据分析与模型仿真的道岔状态分析及评价研究[D].北京：中国铁道科学研究院，2020.QIN Hangyuan. Research on Turnout State Analysis and Evaluation Based on Multi-source Detection Data Analysis and Model Simulation[D]. Beijing:China Academy of Railway Sciences, 2020.
[11]张利斌.基于结构光技术的高速铁路道岔三维检测及应用研究[D].成都：西南交通大学，2016.ZHANG Libin.Research on Three-dimensional Detection and Application of High-speed Railway Turnout Based on Structured Light Technology[D]. Chengdu:Southwest Jiaotong University,2016.
[12]刘芳.有砟桥上无缝道岔轨道几何状态现场检测分析及控制[J].铁道标准设计，2010,54(12):4-8.LIU Fang. Field Inspection and Analysis on Geometrical Conditions of Continuous Turnout Track on Ballast Bridge as Well as Its Control[J]. Railway Standard Design,2010,54(12):4-8.
[13]杨爱红，杨飞，孙加林，等.高速铁路道岔区几何不平顺动态管理限值研究[J].铁道建筑，2022,62(10):35-39.YANG Aihong, YANG Fei, SUN Jialin, et al. Research on Dynamic Management Limits of Geometric Irregularity in Turnout Area of High-speed Railway[J]. Railway building,2022,62(10):35-39.
[14]WU X Q,LIU X X,WANG Z X,et al.A Self-aided Strapdown Inertial Navigation Method Based on Maneuver Constraints and Incremental Observation[J]. Measurement,2022,201:111763.
[15]WANG P,YAN M,ZHANG L,et al.Shock Signal Trend Term Error Correction Method Based on Discrete Wavelet Transform and Low-frequency Oscillator Combination[J].Shock and Vibration,2021,2021(14):9939547.
[16]WANG Y C,XING Y D,ZHANG J. Voronoi Treemap in Manhattan Distance and Chebyshev Distance[J].Information Visualization,2023,22(3):246-264.
基本信息:
中图分类号:U216.3
引用信息:
[1]夏承亮,程朝阳,张二永,等.道岔区轨向结构不平顺实时消除算法[J].铁道建筑,2026,66(05):11-16.
基金信息:
中国铁道科学研究院集团有限公司基金（2023YJ026）
2025-03-14
2025
2025-11-10
2026-04-13
2026
2
2025-06-16
2025-06-16
2025-06-16