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
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摘要
为了保证轨道为列车运行提供可靠的运行基础,铁路工务部门针对轨道经常组织实施修理作业。实施修理作业有三个关键问题(3W)需要确定：作业时间(When)、作业内容(What)及作业地点(Where)。在基于状态的轨道养护维修策略指导下,获取这三个方面信息的核心基础是轨道不平顺状态变化规律。轨检车检测数据(Track Geometry Measurements, TGM)是铁路工务部门把握轨道不平顺状态最重要的状态数据之一。利用TGM研究轨道不平顺状态变化规律提取3W信息有多个方面的问题需要探讨。
本文主要针对其中的两个关键问题进行了研究：一是TGM里程偏差修正问题；二是轨道不平顺状态短期预测问题。本文的研究将为在中国铁路上实现基于状态的轨道养护维修提供理论和技术基础。这种维修策略的实现将能够提高轨道系统可靠性,提高列车运行的安全性,延长轨道设备使用寿命,从而降低轨道设备生命周期成本。
在综合分析国内外专家学者围绕这两个问题提出的解决方案的基础上,本文首先利用均一阈值处理(Uniform Thresholding, UT)、地图匹配(Map Matching)、相关分析和动态规划等技术,研究建立了一个新的修正TGM里程与线路上设备里程之间偏差(简称为第一类里程偏差)的模型,基于关键设备的里程偏差修正模型(Key Equipment based Mileage Error Correction method, KE-BMEC).模型KE-BMEC,首先利用TGM、车站平面布置图和已经在铁路上推广使用近6年的铁路工务管理信息系统(Permanent of Way Management Information System, PWMIS)的数据库(以下简称PWMIS数据库)中存储的工务设备台帐数据,在TGM中确定一些工务设备(称为关键设备)特征点上的采样点；其次根据PWMIS数据库中存储的这些采样点的里程,修正轨检车一次检测产生的整个TGM文件中所有采样点的里程。模型KE-BMEC的性能分析结果表明：经过该模型修正里程偏差后,第一类里程偏差大幅减小了,远小于GPS里程自动修正系统修正后的里程偏差；一般情况下,同一采样点上经过KE-BMEC修正里程的多次检测数据中的里程偏差(简称为第二类里程偏差)小于lm。
其次,利用相关分析、Dynamic Time Warping (DTW)和动态规划等技术研究建立了一个新的修正第二类里程偏差的模型,基于TGM的里程偏差修正模型(TGMbased Mileage Error Correction method, TGM-BMEC)。DTW技术被首次用来解决TGM里程偏差问题。模型TGM-BEMC对经过KE-BMEC处理的TGM(简称为待修正TGM)做进一步里程修正,以尽可能减小第二类里程偏差。以最新的并且经过TGM-BMEC修正的TGM为参考数据,TGM-BMEC首先在参考数据中确定距离待修正TGM中每个采样点最近的采样点(简称为对应采样点),其次利用参考数据记录的对应采样点的里程修正待修正TGM中每个采样点的里程。模型TGM-BMEC的性能分析结果表明：经过该模型修正里程偏差以后,第二类里程偏差小于1个采样点间距0.25m,小于已有里程修正模型处理后的第二类里程偏差。
最后,在以上两个模型修正里程的基础上,根据轨道不平顺状态劣化特点研究建立了一个新的对轨道不平顺状态进行短期预测的模型(Short-Range Prediction Model for Track Irregularities, TI-SRPM)。模型TI-SRPM对每个采样点上所有轨道不平顺指标在未来一段时期(长度由轨检车检测的时间间隔决定)内每一天的幅值进行预测。模型TI-SRPM的性能分析结果表明：TI-SRPM预测的各采样点的幅值与轨检车检测出的各采样点的幅值非常接近；根据TI-SRPM预测的幅值,能够提前一个轨检车检测周期预测出至少80%的轨道不平顺超限,并且幅值较大超限的预测可靠性比幅值较小超限的预测可靠性高；根据TI-SRPM的预测结果,能够提前一个轨检车检测周期较准确地预测出各种长度区段的整体不平顺状态指数,为精细化管理轨道不平顺状态提供状态数据。
To ensure both track elements and track geometry in good condition, railway maintenance of way departments often carry out maintenance and renewal works on track. There are three critical kinds of information (3W) to be accessed, comprising when and where to carry out maintenance works and what maintenance work to be conducted. With the guidance of condiction based track maintenance the key basis for obtaining3W is the track irregularity deterioration. Track Geometry Measurements of Track Geometry Car is one of the most important condition data sources for railway maintenance of way departments to monitor track irregularity. There are several key issues to be addressed to study the track irregularity deterioration according to TGM.
This dissertation focused on two of these key issues. The first deals with the mileage error correction for TGM. The second focuses on the short-range prediction model for track irregularity. Research results in this dissertation will provide theorical and technical basics for implementing condition based track. Such maintenance technique will improve track system reliability and train travelling safety, lengthen track equipment life, and thus reduce Life Cycle Costs for track equipment.
Based on resolutions of these two matters developed by scholars all over the world, firstly, employing Uniform Thresholding (UT), Map Matching, Cross Correlation, and Dynamic Programming, a novel, distinctive model was developed for correcting errors between measured mileages in TGM and track equipment mileages and is named Key Equipment based Mileage Error Correction method (KE-BMEC). These mileage errors are referred to as the first class mileage error throughout this dissertation. There are two steps for KE-BMEC to correct the first class mileage error. The first is to locate from TGM characteristic points of some equipment passed by TGC. For locating these characteristic points, TGM, railway station layout chart, and permanent-of-way equipment records stored in the database of Permanent-of-Way Management Information System (PWMIS) are used. PWMIS has been implemented and used throughout the entire rail network of China since2007. Recorded mileages of all sampling points in TGM are corrected according to mileages of these located characteristic points stored in PWMIS database. From performance analysis results for KE-BMEC, two main conclusions are arrived at as follows:(a) the first class mileage error of processed TGM by KE-BMEC is reduced significantly and thus is far smaller than the one processed by GPS based mileage correction system implemented in TGC; and (b) mileage errors over same sampling points between different inspections of TGC are below1meter in normal circumstances. These mileage errors over same sampling points between different inspections are referred to as the second mileage error.
Secondly, employing Cross Correlation, Dynamic Time Warping (DTW), and Dynamic Programming, a novel model for correcting the second class mileage error was developed and is named TGM based Mileage Error Correction method (TGM-BMEC). DTW is used for correcting TGM mileage errors for the first time. For decreasing the second class mileage error as much as possible, TGM processed by KE-BMEC is treated further with TGM-BMEC. These TGM processed by KE-BMEC but not treated by TGM-BMEC are referred to as pending TGM, whereas the latest TGM processed by both KE-BMEC and TGM-BMEC as reference TGM. There are two steps for TGM to achieve its aim. The first step is to locate the sampling point in the reference TGM with the smallest distance from the one in the pending TGM. And the located sampling point is referred to as the corresponding sampling point. The second is to update for all. sampling points in the pending TGM the mileage according to the mileage of corresponding sampling points. Performance analysis results for TGM-BMEC illustrate that the second class mileage error corrected with TGM-BMEC is smaller than one sampling interval,0.25m, and is less than the one corrected with the existing mileage error correction models.
Finally, based on track irregularity condition evolution characteristics, utilizing TGM processed with KE-BMEC and TGM-BMEC, a novel, distinctive short-range prediction model for track irregularity was developed and is abbreviated to TI-SRPM. TI-SRPM is designed for predicting track irregularity values on each day in a future short period over sampling points. The future short period is determined with the inspection interval of TGC. The following conclusions are drawn from performance analysis results for TI-SRPM as follows:(a) values of predicted track irregularity amplitudes by TI-SRPM are very close to ones measured by TGC;(b) at least80%track irregularity exceptions can be noticed one inspection interval of Track Geometry Car in advance and the prediction reliability for exceptions with bigger amplitudes is higher than that for exceptions with smaller amplitudes; and (c) track condition indices over many track section lengths can also be available one inspection interval in advance and these diverse indices provide data for refined management of track irregularity.
本文主要针对其中的两个关键问题进行了研究：一是TGM里程偏差修正问题；二是轨道不平顺状态短期预测问题。本文的研究将为在中国铁路上实现基于状态的轨道养护维修提供理论和技术基础。这种维修策略的实现将能够提高轨道系统可靠性,提高列车运行的安全性,延长轨道设备使用寿命,从而降低轨道设备生命周期成本。
在综合分析国内外专家学者围绕这两个问题提出的解决方案的基础上,本文首先利用均一阈值处理(Uniform Thresholding, UT)、地图匹配(Map Matching)、相关分析和动态规划等技术,研究建立了一个新的修正TGM里程与线路上设备里程之间偏差(简称为第一类里程偏差)的模型,基于关键设备的里程偏差修正模型(Key Equipment based Mileage Error Correction method, KE-BMEC).模型KE-BMEC,首先利用TGM、车站平面布置图和已经在铁路上推广使用近6年的铁路工务管理信息系统(Permanent of Way Management Information System, PWMIS)的数据库(以下简称PWMIS数据库)中存储的工务设备台帐数据,在TGM中确定一些工务设备(称为关键设备)特征点上的采样点；其次根据PWMIS数据库中存储的这些采样点的里程,修正轨检车一次检测产生的整个TGM文件中所有采样点的里程。模型KE-BMEC的性能分析结果表明：经过该模型修正里程偏差后,第一类里程偏差大幅减小了,远小于GPS里程自动修正系统修正后的里程偏差；一般情况下,同一采样点上经过KE-BMEC修正里程的多次检测数据中的里程偏差(简称为第二类里程偏差)小于lm。
其次,利用相关分析、Dynamic Time Warping (DTW)和动态规划等技术研究建立了一个新的修正第二类里程偏差的模型,基于TGM的里程偏差修正模型(TGMbased Mileage Error Correction method, TGM-BMEC)。DTW技术被首次用来解决TGM里程偏差问题。模型TGM-BEMC对经过KE-BMEC处理的TGM(简称为待修正TGM)做进一步里程修正,以尽可能减小第二类里程偏差。以最新的并且经过TGM-BMEC修正的TGM为参考数据,TGM-BMEC首先在参考数据中确定距离待修正TGM中每个采样点最近的采样点(简称为对应采样点),其次利用参考数据记录的对应采样点的里程修正待修正TGM中每个采样点的里程。模型TGM-BMEC的性能分析结果表明：经过该模型修正里程偏差以后,第二类里程偏差小于1个采样点间距0.25m,小于已有里程修正模型处理后的第二类里程偏差。
最后,在以上两个模型修正里程的基础上,根据轨道不平顺状态劣化特点研究建立了一个新的对轨道不平顺状态进行短期预测的模型(Short-Range Prediction Model for Track Irregularities, TI-SRPM)。模型TI-SRPM对每个采样点上所有轨道不平顺指标在未来一段时期(长度由轨检车检测的时间间隔决定)内每一天的幅值进行预测。模型TI-SRPM的性能分析结果表明：TI-SRPM预测的各采样点的幅值与轨检车检测出的各采样点的幅值非常接近；根据TI-SRPM预测的幅值,能够提前一个轨检车检测周期预测出至少80%的轨道不平顺超限,并且幅值较大超限的预测可靠性比幅值较小超限的预测可靠性高；根据TI-SRPM的预测结果,能够提前一个轨检车检测周期较准确地预测出各种长度区段的整体不平顺状态指数,为精细化管理轨道不平顺状态提供状态数据。
To ensure both track elements and track geometry in good condition, railway maintenance of way departments often carry out maintenance and renewal works on track. There are three critical kinds of information (3W) to be accessed, comprising when and where to carry out maintenance works and what maintenance work to be conducted. With the guidance of condiction based track maintenance the key basis for obtaining3W is the track irregularity deterioration. Track Geometry Measurements of Track Geometry Car is one of the most important condition data sources for railway maintenance of way departments to monitor track irregularity. There are several key issues to be addressed to study the track irregularity deterioration according to TGM.
This dissertation focused on two of these key issues. The first deals with the mileage error correction for TGM. The second focuses on the short-range prediction model for track irregularity. Research results in this dissertation will provide theorical and technical basics for implementing condition based track. Such maintenance technique will improve track system reliability and train travelling safety, lengthen track equipment life, and thus reduce Life Cycle Costs for track equipment.
Based on resolutions of these two matters developed by scholars all over the world, firstly, employing Uniform Thresholding (UT), Map Matching, Cross Correlation, and Dynamic Programming, a novel, distinctive model was developed for correcting errors between measured mileages in TGM and track equipment mileages and is named Key Equipment based Mileage Error Correction method (KE-BMEC). These mileage errors are referred to as the first class mileage error throughout this dissertation. There are two steps for KE-BMEC to correct the first class mileage error. The first is to locate from TGM characteristic points of some equipment passed by TGC. For locating these characteristic points, TGM, railway station layout chart, and permanent-of-way equipment records stored in the database of Permanent-of-Way Management Information System (PWMIS) are used. PWMIS has been implemented and used throughout the entire rail network of China since2007. Recorded mileages of all sampling points in TGM are corrected according to mileages of these located characteristic points stored in PWMIS database. From performance analysis results for KE-BMEC, two main conclusions are arrived at as follows:(a) the first class mileage error of processed TGM by KE-BMEC is reduced significantly and thus is far smaller than the one processed by GPS based mileage correction system implemented in TGC; and (b) mileage errors over same sampling points between different inspections of TGC are below1meter in normal circumstances. These mileage errors over same sampling points between different inspections are referred to as the second mileage error.
Secondly, employing Cross Correlation, Dynamic Time Warping (DTW), and Dynamic Programming, a novel model for correcting the second class mileage error was developed and is named TGM based Mileage Error Correction method (TGM-BMEC). DTW is used for correcting TGM mileage errors for the first time. For decreasing the second class mileage error as much as possible, TGM processed by KE-BMEC is treated further with TGM-BMEC. These TGM processed by KE-BMEC but not treated by TGM-BMEC are referred to as pending TGM, whereas the latest TGM processed by both KE-BMEC and TGM-BMEC as reference TGM. There are two steps for TGM to achieve its aim. The first step is to locate the sampling point in the reference TGM with the smallest distance from the one in the pending TGM. And the located sampling point is referred to as the corresponding sampling point. The second is to update for all. sampling points in the pending TGM the mileage according to the mileage of corresponding sampling points. Performance analysis results for TGM-BMEC illustrate that the second class mileage error corrected with TGM-BMEC is smaller than one sampling interval,0.25m, and is less than the one corrected with the existing mileage error correction models.
Finally, based on track irregularity condition evolution characteristics, utilizing TGM processed with KE-BMEC and TGM-BMEC, a novel, distinctive short-range prediction model for track irregularity was developed and is abbreviated to TI-SRPM. TI-SRPM is designed for predicting track irregularity values on each day in a future short period over sampling points. The future short period is determined with the inspection interval of TGC. The following conclusions are drawn from performance analysis results for TI-SRPM as follows:(a) values of predicted track irregularity amplitudes by TI-SRPM are very close to ones measured by TGC;(b) at least80%track irregularity exceptions can be noticed one inspection interval of Track Geometry Car in advance and the prediction reliability for exceptions with bigger amplitudes is higher than that for exceptions with smaller amplitudes; and (c) track condition indices over many track section lengths can also be available one inspection interval in advance and these diverse indices provide data for refined management of track irregularity.
引文
[l]中国铁道百科全书总编辑委员会《工程与工务》编辑委员会.中国铁道百科全书工程与工务[M].北京：中国铁道出版社,2004：150-173
[2]高亮.轨道工程[M].北京：中国铁道出版社,2010
[3]Weiss B. N. New Research on Acoustics [M]. New York:Nova Science Publishers,2008: 197-220
[4]佐藤吉彦.徐涌,李德浚.新轨道力学[M].北京：中国铁道出版社,2001：98-194
[5]北京铁路工人技师协会.铁路客运专线运输与维护[M].北京：中国铁道出版社,2010：341-353
[6]范俊杰.现代铁路轨道[M].第二版.北京：中国铁道出版社,2004：36-53,221-267
[7]Sadeghi J. and Akbari B. Field Investigation on Effects of Railway Track Geometric Parameters on Rail Wear [J]. Journal of Zhejiang University SCIENCE A,2006,7(11): 1846-1855
[8]Inesa P. et al. Influence of Gauge Width on Rail Side Wear on Track Curves [J]. Journal of Civil Engineering and Management,2006,12(3):255-260
[9]Veit P. and Marschnig S. Sustainability in Track — Precondition for High Speed Traffic [C]. Proceedings of the 2010 Joint Rail Conference. Urbana/Illinois, USA,2010
[10]Sadeghi J. and Askarinejad H. Influences of Track Structure, Geometry and Traffic Parameters on Track Deterioration [J]. IJE Transactions B:Applications,2007,20(3): 292-300
[11]中华人民共和国铁道部.铁运[2006]146号-2006.铁路线路修理规则[s].北京：中国铁道出版社,2010
[12]刘秀波.铁路干线轨道不平顺谱的研究[学位论文].北京：北京交通大学,2006
[13]铁道部基础设施检测中心.轨道检测技术及轨检车设备的运用与维护[M].2007
[14]Hanreich DIW. et al. Track Geometry Measurement Database and Calculation of Equivalent Conicities of the OBB Network [C]. Proceedings of AREMA Annual Conferences 2002. Washington DC, USA.2002
[15]杨爱红.基于RFID的轨检车里程自动校对系统[J].铁路计算机应用,2009,18(10)：39-41
[16]Grantner J. et al. Condition Based Maintenance for Light Trucks [C]. IEEE International Conference on Systems Man and Cybernetics. Istanbul, Turkey,2010
[17]Rabeno E. and Bounds M. Condition Based Maintenance of Military Ground Vehicles [C]. IEEE Aerospace Conference. Big Sky, MT, USA,2009
[18]Holguin L. Condition Based Maintenance (CBM) [C]. IEEE Aerospace Conference. Big Sky, MT, USA,2005
[19]Tian Z.G. and Zuo M.J. Health Condition Prediction of Gears Using a Recurrent Neural Network Approach [J]. IEEE Transactions on Reliability,2010,59(4):700-705
[20]Velasquez J. L. et al. Development and implementation of a Condition Monitoring System in a Substation [C].9th International Conference on Electrical Power Quality and Utilisation. Barcelona, Spain,2007
[21]Bolhuis J. P. v. et al. Monitoring and Diagnostic of Transformer Solid Insulation [J]. IEEE Transactions on Power Delivery,2002,17(2):528-536
[22]Arshad M. and Islam S. M. Power Transformer Condition Assessment Using Oil UV-Spectrophotometry [C]. Annual Report Conference on Electrical Insulation and Dielectric Phenomena. Vancouver, Canada,2007:611-614
[23]Bi P.X. et al. Practice and Strategy for Condition Based Maintenance of Power Supply Equipment [C].2005 IEEE/PES Transmission and Distribution Conference & Exhibition: Asia and Pacific. Dalian, China,2005
[24]Holland-Track Analyst Enterprise [OL]. Website: http://www.industrialmetrics.com/products/trackanalystenterprise/. Retrieved on 2011-6-21
[25]Ensco-TrackIT [OL]. Website: http://www.ensco.com/products-services/rail-technologies/rail-asset-management/trackit.ht m. Retrieved on 2011-6-21
[26]Mermec Group-RAMSYS [OL]. Website: http://www.mermecgroup.com/asset-maintenance-system/339/1/ramsys-product.php. Retrieved on 2011-6-21
[27]Selig E.T. et al. Analyzing and Forecasting Railway Data using linear Data Analysis [C]. Proceedings of 11th International Conference on Computer System Design and Operation in Railways and other Transit Systems. Toledo, Spain,2008
[28]Li H. F. and Xu Y. D. A Method to correct the mileage error in railway track geometry data and its usage [C]. Traffic and Transportation Studies 2010. Kunming, China,2010: 1130-1136
[29]隋国柱,李海峰,许玉德.轨道几何状态检测数据里程校正算法研究[J].交通信息与安全,2009,27(6)：18-21
[30]Kesler J. K. et al. Automated Track Location Identification Using Measured Track Data [P]. USA:5791063,1998, August 11
[31]O'Brien D. K. Optimal Estimation and Rail Tracking Analysis [Dissertation]. Massachusetts, USA:University of Massachusetts Lowell,2005
[32]许玉德,李海峰,戴月辉.轨道交通工务管理[M].上海：同济大学出版社,2007：55-66
[33]周宇,许玉德,李浩然.轨道不平顺非线性预测模型[J].交通运输工程学报,2004,4(4)：21-24
[34]Bartram D. et al. A Computational Intelligence Approach to Railway Track Intervention Planning [M].In:Studies in Computational Intelligence. Berlin:Springerlink,2008,86: 163-198
[35]KAWAGUCHI A. et al. Actual Data Analysis of Alignment Irregularity Growth and its Prediction Model [J]. QR of RTRI,2005,46(4):262-268.
[36]Quiroga L. M. and Schnieder E. A Heuristic Approach to Railway Track Maintenance Scheduling [C]. Proceedings of 12th International Conference on Computer System Design and Operation in Railways and other Transit Systems. Beijing, China,2010:687-699
[37]Dell'Orco M. et al. New Decision Support System for Optimization of Rail Track Maintenance Planning Based on Adaptive Neuro-fuzzy Inference System [J]. Transportation Research Record:Journal of the Transportation Research Board,2008, (2043):49-54.
[38]Jovanovic S. Railway Track Quality Assessment and Related Decision Making [C]. Proceedings of AREMA annual conferences 2006. Kentucky, USA,2006
[39]陈宪麦,王澜,杨凤春,柴雪松,吴旺青.用于铁路轨道不平顺预测的综合因子法[J].中国铁道科学,2006,27(6)：27-3 1
[40]陈宪麦.轨道不平顺时频域分析及预测方法的研究[学位论文].北京：铁道科学研究院,2006：95-112
[41]常欢,刘仍奎,方圆.改进的轨道质量指数线性预测模型研究[J].铁路计算机应用,2010,19(6)：1-3
[42]Yang A. et al. A New integrating Factor Method for Track Irregularity Multistage Prediction Based on Track Maintenance Standard [C]. Proceeds of the 7th World Congress on High Speed Rail. Beijing, China,2010
[43]Wang F.T. et al. Research on Track Alignment Irregularity Prediction Models [C]. Proceedings of the 2010 Joint Rail Conference. Urbana, Illinois, USA,2010
[44]Meier-Hirmer C. et al. A Decision Support System for Track Maintenance [C]. Proceedings of 10th International Conference on Computer System Design and Operation in Railways and other Transit Systems. Prague, Czech Republic.2010,2010:217-226
[45]Meier-Hirmer C.et al. Maintenance Optimization for a System with a Gamma Deterioration Process and Intervention Delay:Application to Track-Maintenance [J]. Proceedings of the Institution of Mechanical Engineers, Part O:Journal of Risk and Reliability,2009,223(189): 189-198
[46]高建敏.铁路有砟轨道下沉及高低不平顺发展预测研究[学位论文].成都：西南交通大学,2008：89-139
[47]王建西,李海峰,许玉德.基于概率分布推移变化的铁路轨道几何状态评定与预测方法[J].中国铁道科学,2008,29(5)：3 1-34
[48]许玉德,吴纪才.利用线性预测模型分析轨道不平顺发展[J].石家庄铁道学院学报,2005,18(1)：6-9
[49]Chang H.et al. Multistage Linear Prediction Model for Irregularity of Longitudinal Level over Unit Railway Sections [C]. Proceedings of 12th International Conference on Computer System Design and Operation in Railways and other Transit Systems. Beijing, China,2010: 641-650
[50]Chang H. et al. Multistage Linear Prediction Model of Track Quality Index [C]. Proceedings of the 7th International Conference on Traffic and Transportation Studies. Kunming, China, 2010:1183-1192
[51]曲建军,高亮,张新奎,辛涛.基于灰色GM(1.1)非等时距修正模型的轨道质量预测[J].中国铁道科学,2009,30(3)：5-8
[52]曲建军,高亮,田新宇,辛涛.基于灰色理论的轨道几何状态中长期时变参数预测模型的研究[J].铁道学报,2010,32(2)：55-59.
[53]曲建军,高亮,辛涛,郑晓莉.基于改进灰色-马尔可夫链的轨道不平顺发展预测方法[J].北京交通大学学报,2010,34(4)：107-111
[54]Esveld C. Modern railway track [M]. Second Edition. The Netherlands:Delft University of Technology Pub. Service,2001:475-565,349-406
[55]Zhang Y. J. and Clouse A. L. An Integrated Track Data Management System [C]. Proceedings of AREMA Annual Conferences 2002. Washington DC, USA,2002
[56]Saab S.S. et al. Compensation of Axle-Generator Errors Due to Wheel Slip and Slide [J]. IEEE Transactions on Vehicular Technology,2002,51(3):577-587
[57]邓学通,叶一鸣.准高速轨检车检测原理及应用[M].北京：中国铁道出版社,2004
[58]郭年根,孙美,孙远运,徐文荣,张树艳.铁路工务管理信息系统的设计与实现[J].计算机系统应用,2003,12(7)：17-20
[59]张树艳,郭年根,吕春英.钢轨伤损管理信息系统的设计与实现[J].铁路计算机应用,2005,14(2)：17-20
[60]Mundrey J. S. Railway Track Engineering [M]. India:Tata McGraw-Hill Education,1988: 104-134,135-161
[61]铁道部运输局基础部.铁路线路修理规则条文说明[M].北京：中国铁道出版社,2008
[62]Profillidis V. A. Railway Management and Engineering [M]. Third Edition. United Kingdom:Ashgate Publishing Ltd,2006:309-321,289-290
[63]郝瀛.铁道工程[M].北京：中国铁道出版社,2000：93-100
[64]铁道部工务局.铁路工务技术手册-道岔[M].第二版.北京：中国铁道出版社,2005：172-178,15-16,33-35
[65]Iwnicki S. Handbook of Railway Vehicle Dynamics [M]. England:Taylor & Francis Group, 2006:158-159-
[66]曹玉成.道岔附带曲线养护整正的方法改进[J].包钢科技,2007,33(4)：75-76
[67]铁道部科技教育司,铁道部劳动和卫生司,铁道部人才服务中心.线路工[M].北京：中国铁道出版社,2001：119-119
[68]Nixon M. S. and Aguado A. S. Feature Extraction and Image Processing [M]. Second Edition. United Kingdom:Academic Press,2008:183-239
[69]Oracle PL/SQL [OL]. Website: http://www.oracle.com/technetwork/database/features/plsql/index.html. Retrieved on 2011-9-25
[70]Urman S. et al. Oracle Database 10g PL/SQL Programming [M]. United States of America: McGraw-Hill/Osborne,2004
[71]Quddus M. A. et al. Integrity of Map-Matching Algorithms [J]. Transportation Research Part C:Emerging Technologies,2006,14(4):283-302
[72]Quddus M. A.et al. Current Map-Matching Algorithms for Transport Applications: State-of-the-art and Future Research Directions [J]. Transportation Research Part C: Emerging Technologies,2007,15(5):312-328
[73]Velga N. R. et al. Developing an Enhanced Weight-based Topological Map-Matching Algorithm for Intelligent Transport Systems [J]. Transportation Research Part C:Emerging Technologies,2009,17(6):672-683
[74]Greenfeld J.S. Matching GPS Observations to Location on a Digital Map [C]. The 81 st Annual Meeting of Transportation Research Board. Washington. D.C., USA,2002
[75]Pfoser D. and Jensen C. S. Capturing the Uncertainty of Moving-Object Representations [C]. Proceedings of the 6th International Symposium on Advances in Spatial Databases. Hong Kong, China.1999. London:United Kingdom:Springer-Verlag,1999:111-132
[76]Sniedovich M. Dynamic Programming Foundation and Principles [M]. Second Edition. England:Taylor & Francis Group,2011:358-377
[77]刘思峰,党耀国,方志耕,谢乃明.灰色系统理论及其应用[M].第五版.北京：科学出版社,2010：62-96
[78]中华人民共和国铁道部.铁建设函[2005]285号-2005.新建时速200公里客货共线铁路设计暂行规定[S].北京：中国铁道出版社,2006：6-8
[79]中华人民共和国铁道部.铁建设[2007]47号-2007.新建时速300～350公里客运专线铁路设计暂行规定[S].北京：中国铁道出版社,2007：15-20
[80]中华人民共和国国家质量监督检验检疫总局,中华人民共和国建设部.GB 50090-2006.铁路线路设计规范[S].北京：中国标准出版社,2006,7-15
[81]易思蓉,郝瀛.铁路选线设计[M].四川：西南交通大学出版社,2005：69-73
[82]蔡力律.城轨磁悬浮交通线路平面技术条件研究[学位论文].成都：西南交通大学,2008：21-25
[83]Richard Weber. Optimization. United Kingdom [OL]. The Cambridge University Mathematical Society. Springer-Verlag.2010.3. (Not Published, Website: http://www.archim.org.uk/lecturenotes/ib/O.pdf. Retrieved on 2011-10-11)
[84]Pardalos P. M. and Romeijn H. E. Handbook of Global Optimization, Volume 2. The Netherlands [M]. The Nethelands:Kluwer Academic Publishers,2002:151-177
[85]Steenbergen M.J.M.M. Wheel-Rail Interaction at Short-Wave Irregularities [Dissertation]. The Netherlands:Delft University of Technology,2008.
[86]Iyengar R. N. and Jaiswal O. R. Random field modeling of railway track irregularities [J]. Journal of Transportation Engineering,1995,121(4):303-308
[87]Theoretical Foundations [OL]. Website: http://www.cs.umn.edu/research/research_areas/theoretical_found.php. Retrieved on 2011-11-30
[88]Zimmermann H. J. Fuzzy Set Theory and its Applications [M]. Fourth Edition. Massachusetts, USA:Kluwer Academic Publishers,2001:1-8
[89]Chu S. et al. Iterative Deepening Dynamic Time Warping for Time Series [C]. Proceedings of the 2002 SIAM International Conference on Data Mining. Crystal City at Ronald Reagan National Airport, Arlington, VA, US,2002
[90]Kwong K. et al. Arterial travel time estimation based on vehicle re-identification using wireless magnetic sensors [J]. Transportation Research Part C:Emerging Technologies, 2009,17(6):586-606
[91]Taylor J. et al. Calibrating Dynamic Car-Following Model Using Vehicle Trajectory Data:A Dynamic Time Warping Approach [C]. The 91th Annual Meeting of Transportation Research Board. Washington. D.C., USA,2012
[92]Sakoe H. and Chiba S. Dynamic Programming Algorithm Optimization for Spoken Word Recognition [J]. IEEE Transactions on Acoustics, Speech, and Signal Processing, 1978,26(1):43-49
[93]Sankoff D. and Kruskal J. Time Warps, String Edits, and Macromolecules [M]. California, USA:CSLI Publications,1999
[94]Chattefuee S. and Hadi A. S. Regression Analysis by Example [M]. Fourth Edition. New Jersey, USA:John Wiley & Sons, Inc,2006:21-45
[2]高亮.轨道工程[M].北京：中国铁道出版社,2010
[3]Weiss B. N. New Research on Acoustics [M]. New York:Nova Science Publishers,2008: 197-220
[4]佐藤吉彦.徐涌,李德浚.新轨道力学[M].北京：中国铁道出版社,2001：98-194
[5]北京铁路工人技师协会.铁路客运专线运输与维护[M].北京：中国铁道出版社,2010：341-353
[6]范俊杰.现代铁路轨道[M].第二版.北京：中国铁道出版社,2004：36-53,221-267
[7]Sadeghi J. and Akbari B. Field Investigation on Effects of Railway Track Geometric Parameters on Rail Wear [J]. Journal of Zhejiang University SCIENCE A,2006,7(11): 1846-1855
[8]Inesa P. et al. Influence of Gauge Width on Rail Side Wear on Track Curves [J]. Journal of Civil Engineering and Management,2006,12(3):255-260
[9]Veit P. and Marschnig S. Sustainability in Track — Precondition for High Speed Traffic [C]. Proceedings of the 2010 Joint Rail Conference. Urbana/Illinois, USA,2010
[10]Sadeghi J. and Askarinejad H. Influences of Track Structure, Geometry and Traffic Parameters on Track Deterioration [J]. IJE Transactions B:Applications,2007,20(3): 292-300
[11]中华人民共和国铁道部.铁运[2006]146号-2006.铁路线路修理规则[s].北京：中国铁道出版社,2010
[12]刘秀波.铁路干线轨道不平顺谱的研究[学位论文].北京：北京交通大学,2006
[13]铁道部基础设施检测中心.轨道检测技术及轨检车设备的运用与维护[M].2007
[14]Hanreich DIW. et al. Track Geometry Measurement Database and Calculation of Equivalent Conicities of the OBB Network [C]. Proceedings of AREMA Annual Conferences 2002. Washington DC, USA.2002
[15]杨爱红.基于RFID的轨检车里程自动校对系统[J].铁路计算机应用,2009,18(10)：39-41
[16]Grantner J. et al. Condition Based Maintenance for Light Trucks [C]. IEEE International Conference on Systems Man and Cybernetics. Istanbul, Turkey,2010
[17]Rabeno E. and Bounds M. Condition Based Maintenance of Military Ground Vehicles [C]. IEEE Aerospace Conference. Big Sky, MT, USA,2009
[18]Holguin L. Condition Based Maintenance (CBM) [C]. IEEE Aerospace Conference. Big Sky, MT, USA,2005
[19]Tian Z.G. and Zuo M.J. Health Condition Prediction of Gears Using a Recurrent Neural Network Approach [J]. IEEE Transactions on Reliability,2010,59(4):700-705
[20]Velasquez J. L. et al. Development and implementation of a Condition Monitoring System in a Substation [C].9th International Conference on Electrical Power Quality and Utilisation. Barcelona, Spain,2007
[21]Bolhuis J. P. v. et al. Monitoring and Diagnostic of Transformer Solid Insulation [J]. IEEE Transactions on Power Delivery,2002,17(2):528-536
[22]Arshad M. and Islam S. M. Power Transformer Condition Assessment Using Oil UV-Spectrophotometry [C]. Annual Report Conference on Electrical Insulation and Dielectric Phenomena. Vancouver, Canada,2007:611-614
[23]Bi P.X. et al. Practice and Strategy for Condition Based Maintenance of Power Supply Equipment [C].2005 IEEE/PES Transmission and Distribution Conference & Exhibition: Asia and Pacific. Dalian, China,2005
[24]Holland-Track Analyst Enterprise [OL]. Website: http://www.industrialmetrics.com/products/trackanalystenterprise/. Retrieved on 2011-6-21
[25]Ensco-TrackIT [OL]. Website: http://www.ensco.com/products-services/rail-technologies/rail-asset-management/trackit.ht m. Retrieved on 2011-6-21
[26]Mermec Group-RAMSYS [OL]. Website: http://www.mermecgroup.com/asset-maintenance-system/339/1/ramsys-product.php. Retrieved on 2011-6-21
[27]Selig E.T. et al. Analyzing and Forecasting Railway Data using linear Data Analysis [C]. Proceedings of 11th International Conference on Computer System Design and Operation in Railways and other Transit Systems. Toledo, Spain,2008
[28]Li H. F. and Xu Y. D. A Method to correct the mileage error in railway track geometry data and its usage [C]. Traffic and Transportation Studies 2010. Kunming, China,2010: 1130-1136
[29]隋国柱,李海峰,许玉德.轨道几何状态检测数据里程校正算法研究[J].交通信息与安全,2009,27(6)：18-21
[30]Kesler J. K. et al. Automated Track Location Identification Using Measured Track Data [P]. USA:5791063,1998, August 11
[31]O'Brien D. K. Optimal Estimation and Rail Tracking Analysis [Dissertation]. Massachusetts, USA:University of Massachusetts Lowell,2005
[32]许玉德,李海峰,戴月辉.轨道交通工务管理[M].上海：同济大学出版社,2007：55-66
[33]周宇,许玉德,李浩然.轨道不平顺非线性预测模型[J].交通运输工程学报,2004,4(4)：21-24
[34]Bartram D. et al. A Computational Intelligence Approach to Railway Track Intervention Planning [M].In:Studies in Computational Intelligence. Berlin:Springerlink,2008,86: 163-198
[35]KAWAGUCHI A. et al. Actual Data Analysis of Alignment Irregularity Growth and its Prediction Model [J]. QR of RTRI,2005,46(4):262-268.
[36]Quiroga L. M. and Schnieder E. A Heuristic Approach to Railway Track Maintenance Scheduling [C]. Proceedings of 12th International Conference on Computer System Design and Operation in Railways and other Transit Systems. Beijing, China,2010:687-699
[37]Dell'Orco M. et al. New Decision Support System for Optimization of Rail Track Maintenance Planning Based on Adaptive Neuro-fuzzy Inference System [J]. Transportation Research Record:Journal of the Transportation Research Board,2008, (2043):49-54.
[38]Jovanovic S. Railway Track Quality Assessment and Related Decision Making [C]. Proceedings of AREMA annual conferences 2006. Kentucky, USA,2006
[39]陈宪麦,王澜,杨凤春,柴雪松,吴旺青.用于铁路轨道不平顺预测的综合因子法[J].中国铁道科学,2006,27(6)：27-3 1
[40]陈宪麦.轨道不平顺时频域分析及预测方法的研究[学位论文].北京：铁道科学研究院,2006：95-112
[41]常欢,刘仍奎,方圆.改进的轨道质量指数线性预测模型研究[J].铁路计算机应用,2010,19(6)：1-3
[42]Yang A. et al. A New integrating Factor Method for Track Irregularity Multistage Prediction Based on Track Maintenance Standard [C]. Proceeds of the 7th World Congress on High Speed Rail. Beijing, China,2010
[43]Wang F.T. et al. Research on Track Alignment Irregularity Prediction Models [C]. Proceedings of the 2010 Joint Rail Conference. Urbana, Illinois, USA,2010
[44]Meier-Hirmer C. et al. A Decision Support System for Track Maintenance [C]. Proceedings of 10th International Conference on Computer System Design and Operation in Railways and other Transit Systems. Prague, Czech Republic.2010,2010:217-226
[45]Meier-Hirmer C.et al. Maintenance Optimization for a System with a Gamma Deterioration Process and Intervention Delay:Application to Track-Maintenance [J]. Proceedings of the Institution of Mechanical Engineers, Part O:Journal of Risk and Reliability,2009,223(189): 189-198
[46]高建敏.铁路有砟轨道下沉及高低不平顺发展预测研究[学位论文].成都：西南交通大学,2008：89-139
[47]王建西,李海峰,许玉德.基于概率分布推移变化的铁路轨道几何状态评定与预测方法[J].中国铁道科学,2008,29(5)：3 1-34
[48]许玉德,吴纪才.利用线性预测模型分析轨道不平顺发展[J].石家庄铁道学院学报,2005,18(1)：6-9
[49]Chang H.et al. Multistage Linear Prediction Model for Irregularity of Longitudinal Level over Unit Railway Sections [C]. Proceedings of 12th International Conference on Computer System Design and Operation in Railways and other Transit Systems. Beijing, China,2010: 641-650
[50]Chang H. et al. Multistage Linear Prediction Model of Track Quality Index [C]. Proceedings of the 7th International Conference on Traffic and Transportation Studies. Kunming, China, 2010:1183-1192
[51]曲建军,高亮,张新奎,辛涛.基于灰色GM(1.1)非等时距修正模型的轨道质量预测[J].中国铁道科学,2009,30(3)：5-8
[52]曲建军,高亮,田新宇,辛涛.基于灰色理论的轨道几何状态中长期时变参数预测模型的研究[J].铁道学报,2010,32(2)：55-59.
[53]曲建军,高亮,辛涛,郑晓莉.基于改进灰色-马尔可夫链的轨道不平顺发展预测方法[J].北京交通大学学报,2010,34(4)：107-111
[54]Esveld C. Modern railway track [M]. Second Edition. The Netherlands:Delft University of Technology Pub. Service,2001:475-565,349-406
[55]Zhang Y. J. and Clouse A. L. An Integrated Track Data Management System [C]. Proceedings of AREMA Annual Conferences 2002. Washington DC, USA,2002
[56]Saab S.S. et al. Compensation of Axle-Generator Errors Due to Wheel Slip and Slide [J]. IEEE Transactions on Vehicular Technology,2002,51(3):577-587
[57]邓学通,叶一鸣.准高速轨检车检测原理及应用[M].北京：中国铁道出版社,2004
[58]郭年根,孙美,孙远运,徐文荣,张树艳.铁路工务管理信息系统的设计与实现[J].计算机系统应用,2003,12(7)：17-20
[59]张树艳,郭年根,吕春英.钢轨伤损管理信息系统的设计与实现[J].铁路计算机应用,2005,14(2)：17-20
[60]Mundrey J. S. Railway Track Engineering [M]. India:Tata McGraw-Hill Education,1988: 104-134,135-161
[61]铁道部运输局基础部.铁路线路修理规则条文说明[M].北京：中国铁道出版社,2008
[62]Profillidis V. A. Railway Management and Engineering [M]. Third Edition. United Kingdom:Ashgate Publishing Ltd,2006:309-321,289-290
[63]郝瀛.铁道工程[M].北京：中国铁道出版社,2000：93-100
[64]铁道部工务局.铁路工务技术手册-道岔[M].第二版.北京：中国铁道出版社,2005：172-178,15-16,33-35
[65]Iwnicki S. Handbook of Railway Vehicle Dynamics [M]. England:Taylor & Francis Group, 2006:158-159-
[66]曹玉成.道岔附带曲线养护整正的方法改进[J].包钢科技,2007,33(4)：75-76
[67]铁道部科技教育司,铁道部劳动和卫生司,铁道部人才服务中心.线路工[M].北京：中国铁道出版社,2001：119-119
[68]Nixon M. S. and Aguado A. S. Feature Extraction and Image Processing [M]. Second Edition. United Kingdom:Academic Press,2008:183-239
[69]Oracle PL/SQL [OL]. Website: http://www.oracle.com/technetwork/database/features/plsql/index.html. Retrieved on 2011-9-25
[70]Urman S. et al. Oracle Database 10g PL/SQL Programming [M]. United States of America: McGraw-Hill/Osborne,2004
[71]Quddus M. A. et al. Integrity of Map-Matching Algorithms [J]. Transportation Research Part C:Emerging Technologies,2006,14(4):283-302
[72]Quddus M. A.et al. Current Map-Matching Algorithms for Transport Applications: State-of-the-art and Future Research Directions [J]. Transportation Research Part C: Emerging Technologies,2007,15(5):312-328
[73]Velga N. R. et al. Developing an Enhanced Weight-based Topological Map-Matching Algorithm for Intelligent Transport Systems [J]. Transportation Research Part C:Emerging Technologies,2009,17(6):672-683
[74]Greenfeld J.S. Matching GPS Observations to Location on a Digital Map [C]. The 81 st Annual Meeting of Transportation Research Board. Washington. D.C., USA,2002
[75]Pfoser D. and Jensen C. S. Capturing the Uncertainty of Moving-Object Representations [C]. Proceedings of the 6th International Symposium on Advances in Spatial Databases. Hong Kong, China.1999. London:United Kingdom:Springer-Verlag,1999:111-132
[76]Sniedovich M. Dynamic Programming Foundation and Principles [M]. Second Edition. England:Taylor & Francis Group,2011:358-377
[77]刘思峰,党耀国,方志耕,谢乃明.灰色系统理论及其应用[M].第五版.北京：科学出版社,2010：62-96
[78]中华人民共和国铁道部.铁建设函[2005]285号-2005.新建时速200公里客货共线铁路设计暂行规定[S].北京：中国铁道出版社,2006：6-8
[79]中华人民共和国铁道部.铁建设[2007]47号-2007.新建时速300～350公里客运专线铁路设计暂行规定[S].北京：中国铁道出版社,2007：15-20
[80]中华人民共和国国家质量监督检验检疫总局,中华人民共和国建设部.GB 50090-2006.铁路线路设计规范[S].北京：中国标准出版社,2006,7-15
[81]易思蓉,郝瀛.铁路选线设计[M].四川：西南交通大学出版社,2005：69-73
[82]蔡力律.城轨磁悬浮交通线路平面技术条件研究[学位论文].成都：西南交通大学,2008：21-25
[83]Richard Weber. Optimization. United Kingdom [OL]. The Cambridge University Mathematical Society. Springer-Verlag.2010.3. (Not Published, Website: http://www.archim.org.uk/lecturenotes/ib/O.pdf. Retrieved on 2011-10-11)
[84]Pardalos P. M. and Romeijn H. E. Handbook of Global Optimization, Volume 2. The Netherlands [M]. The Nethelands:Kluwer Academic Publishers,2002:151-177
[85]Steenbergen M.J.M.M. Wheel-Rail Interaction at Short-Wave Irregularities [Dissertation]. The Netherlands:Delft University of Technology,2008.
[86]Iyengar R. N. and Jaiswal O. R. Random field modeling of railway track irregularities [J]. Journal of Transportation Engineering,1995,121(4):303-308
[87]Theoretical Foundations [OL]. Website: http://www.cs.umn.edu/research/research_areas/theoretical_found.php. Retrieved on 2011-11-30
[88]Zimmermann H. J. Fuzzy Set Theory and its Applications [M]. Fourth Edition. Massachusetts, USA:Kluwer Academic Publishers,2001:1-8
[89]Chu S. et al. Iterative Deepening Dynamic Time Warping for Time Series [C]. Proceedings of the 2002 SIAM International Conference on Data Mining. Crystal City at Ronald Reagan National Airport, Arlington, VA, US,2002
[90]Kwong K. et al. Arterial travel time estimation based on vehicle re-identification using wireless magnetic sensors [J]. Transportation Research Part C:Emerging Technologies, 2009,17(6):586-606
[91]Taylor J. et al. Calibrating Dynamic Car-Following Model Using Vehicle Trajectory Data:A Dynamic Time Warping Approach [C]. The 91th Annual Meeting of Transportation Research Board. Washington. D.C., USA,2012
[92]Sakoe H. and Chiba S. Dynamic Programming Algorithm Optimization for Spoken Word Recognition [J]. IEEE Transactions on Acoustics, Speech, and Signal Processing, 1978,26(1):43-49
[93]Sankoff D. and Kruskal J. Time Warps, String Edits, and Macromolecules [M]. California, USA:CSLI Publications,1999
[94]Chattefuee S. and Hadi A. S. Regression Analysis by Example [M]. Fourth Edition. New Jersey, USA:John Wiley & Sons, Inc,2006:21-45