Historical Data Matching Method of Static Track Geometry Measurement andIts Performance Evaluation
WEI Hui1, YANG Fei2, WU Shifeng3, ZHU Lei4, YAO Hanxing4, REN Xiaoyi4
1. Jiangxi University of Technology, Nanchang, Jiangxi 330098, China; 2. China Academy of Railway Sciences, Beijing 100081, China; 3. China Railway Nanchang Group Co. Ltd, Nanchang, Jiangxi 330103, China; 4. Nanchang University, Nanchang, Jiangxi 330031, China
Abstract:Research purposes: Historical data matching is the prerequisite and basis for data mining of orbital track geometric state. At present, matching based on external features is difficult to achieve sub-meter matching accuracy, and matching algorithms based on similarity are highly complex. To solve the above problems, this paper took static track inspection data as the research object, measured the similarity of data through dynamic time warping, and estimated the mileage deviation with the wrapped path, thus constructing an accurate matching method of static inspection historical data. On this basis, this paper discussed the matching performance evaluation, and introduced the synchronicity indicators on the basis of correlation and accuracy of the evaluation. Finally, the daily inspection data of a high-speed railway between January 15, 2021 and July 7, 2021 were used to verify the preceding matching method. Research conclusions:(1) Compared with methods based on external features, the matching method described in this paper has better correlation, synchronization and accuracy. (2) Simulating maintenance operation by means of deforming the waveform, this method is robust to the maintenance operation and data loss. (3) This method is dispensed with external features, so it has the characteristics of low cost and good adaptability, and can be used for deep mining of track state information.
徐鹏. 铁路轨检车检测数据里程偏差修正模型及轨道不平顺状态预测模型研究[D]. 北京:北京交通大学, 2012.Xu Peng. Mileage Correction Model for Track Geometry Data from Track Geometry Car & Track Irregularity Prediction Model[D]. Beijing:Beijing Jiaotong University, 2012.
[2]
陶捷, 朱洪涛. 非接触式轨枕识别测量装置及测量方法:中国,CN201210262250.3[P]. 2012.12.12.Tao Jie, Zhu Hongtao. A Measurig Method of Non-Contact Recognizing Device for Railway Sleeper:China, CN201210262250.3[P]. 2012.12.12.
[3]
甄静,杨超,许贵阳. 0号高速综合检测列车定位同步技术[J]. 中国铁路, 2011(2):56-58.Zhen Jing, Yang Chao, Xu Guiyang. Synchronous Positioning Technology of No.0 High Speed Comprehensive Inspection Train[J]. Chinese Railways, 2011(2):56-58.
[4]
汪鑫,王平,陈嵘,等. 基于多次波形匹配的高速铁路动检数据里程误差评估与修正[J]. 铁道学报, 2020(2):110-116.Wang Xin,Wang Ping,Chen Rong, etc. Mileage Error Estimation and Correction for High-speed Railway Track Inspection Data Based on Multiple Data Waveform[J]. Journal of the China Railway Society, 2020(2):110-116.
[5]
Xu Peng,Liu Rengkui,Sun Quanxin, etc. Dynamic-time -warping-based Measurement Data Alignment Model for Condition-based Railroad Track Maintenance[J]. IEEE Transactions on Intelligent Transportation Systems, 2015(2):799-812.
[6]
魏晖,杨飞,朱洪涛,等. 基于DTW的轨道动静态检查数据的匹配方法[J]. 铁道科学与工程学报, 2022(1):78-86.Wei Hui, Yang Fei, Zhu Hongtao, etc. A Matching Method for Dynamic and Static Inspection Data of Track Based on Dynamic Time Warping[J]. Journal of Railway Science and Engineering, 2022(1):78-86.
[7]
陈海燕,刘晨晖,孙博. 时间序列数据挖掘的相似性度量综述[J]. 控制与决策, 2017(1):1-11.Chen Haiyan, Liu Chenhui, Sun Bo. Survey on Similarity Measurement of Time Series Data Mining[J]. Control and Decision, 2017(1):1-11.
[8]
Rakthanmanon T,Campana B,Mueen A, etc. Searching and Mining Trillions of Time Series Subsequences under Dynamic Time Warping[C]//Proceedings of the 18th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, New York:Association for Computing Machinery, 2012:262-270.
[9]
刘宝生,闫莉萍,周东华. 几种经典相似性度量的比较研究[J]. 计算机应用研究, 2006(11):1-3.Liu Baosheng, Yan Liping, Zhou Donghua. Comparison of Some Classical Similarity Measures[J]. Application Research of Computers, 2006(11):1-3.
[10]
蒋贵虎,陈万忠,马迪,等. 基于ITD和PLV的四类运动想象脑电分类方法研究[J]. 仪器仪表学报, 2019(5):195-202.Jiang Guihu, Chen Wanzhong, Ma Di, etc. Research on Four-class Motor Imagery EEG Classification Method Based on ITD and PLV[J]. Chinese Journal of Scientific Instrument, 2019(5):195-202.