轮轨力连续测试方法及车轮失圆的检测与识别研究
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摘要
对列车关键部件进行实时的安全监测是确保列车运行安全性的重要手段,通过实时的记录、分析与诊断,及时发现存在严重安全隐患的车辆,并查找出故障的位置与原因,实施扣车检修,对保障铁路行车安全具有十分积极的作用,具有显著的社会与经济效益。本文正是在中国铁路跨越式发展背景下,以轮轨动态作用安全为主题,从列车和轨道线路整体大系统的角度,综合研究列车与轨道动态相互作用安全问题。主要进行了如下的研究工作：
(1)轮轨力连续测试的实现
根据轮轨相互作用的特点和轨道的约束条件,通过仿真计算研究了轮轨垂向力与横向力测试问题,详细分析了荷载在移动过程中轨道的响应的变化以及不同位置测点的应变输出,确定了连续测试方案中测点的布置位置,并通过室内试验进行了验证。以此为依据,在现场测试中提出了一种基于径向基函数神经网络的轮轨力连续测试方法,以及一种基于灰色理论、遗传算法与神经网络的组合模型的轮轨力连续测试方法。通过现场试验,证明了其高精度与可行性,并降低了传感器误差对测试结果的影响。
(2)轮轨力解耦问题的研究
通过仿真计算研究了横向力作用下的垂向力测点的应变输出以及垂向力作用下的横向力测点的应变输出,发现横向力和垂向力间存在串扰问题,同时在室内试验中也发现了此现象。针对此问题,本文提出使用一种改进的独立分量分析算法对轮轨力的串扰进行解耦,为高精度轮轨力测试提供了一种新的手段。
(3)车辆轨道耦合动力学仿真分析模型的建立
以车辆轨道耦合动力学理论为基础,以高速列车为研究对象,采用铁道车辆动力学仿真技术,系统地分析车辆轨道耦合系统的轮轨接触和相互作用问题,分别建立车辆和轨道结构的力学模型和动力学仿真分析模型,采用Hertz接触理论和Kalker滚动接触理论,研究轮轨滚动接触关系中车轮失圆时的轮轨相互作用的过程和轮轨作用力,为研究车轮失圆对轮轨系统动力特征的影响提供基础。
(4)车轮失圆对轮轨动力作用的影响分析
车轮失圆是影响行车安全的重大隐患。对车轮失圆形式进行定义分类,结合所建立的车辆轨道耦合动力学仿真分析模型,研究车轮扁疤、车轮周期性多边形磨耗等常见的车轮失圆类型所造成的轮轨冲击振动的特征,从而为轮轨力实时监测系统的建立提供理论基础。
(5)基于轮轨力的车轮失圆的识别
利用车辆轨道耦合动力学仿真模型,计算了80组不同长度、不同深度的车轮扁疤作用下的轮轨冲击力,根据轮轨力的大小将车轮分为3种类型：正常车轮、问题车轮和严重问题车轮。首先通过特征提取方法对原始数据进行压缩,避免维数灾难,再详细分析了3种分类器：BP神经网络、学习向量量化神经网络和支持向量机对车轮进行分类识别情况。
(6)基于轴箱振动加速度的车轮失圆的识别
车轮失圆将会引起轴箱的异常振动,通过安装在轴箱上的加速度传感器可能对车轮失圆情况进行识别。本文提出一种基于改进的EMD的Hilbert谱方法,利用实测数据及仿真计算,有效地对车轮失圆问题进行了定性的识别,该方法不仅能判断车轮是否存在失圆状况,还能判断失圆类型是车轮扁疤还是车轮多边形化,且不受车速的影响。同时,利用小波包分解还能对失圆程度进行定量判断。
Real-time safety monitoring of train key component is an important means to ensure the security of train operation. Through the real-time recording, analysis and diagnosis, timely detect vehicles which exists serious safety hazard and find out the location of the fault and its reason, furthermore, implement of vehicle maintenance, which has a very positive role in the protection of railway traffic safety and significant social and economic benefits. This paper is in the context of the leaps and bounds development of the China railway, use the wheel-rail dynamic impact as the theme, from the point of view vehicle and track overall system, comprehensively research vehicle-track dynamic interaction security issue. This paper mainly carries out the following research works:
(1) Realization of wheel-rail force continuous measurement
According to wheel-rail interaction characteristics and the restriction condition of track, the single point measure of wheel-rail vertical force and lateral force are studied by means of simulation calculation, rail response change when load moves and stain outputs of measure points in different position are detailed analysed, finally the measure points arrangement position of continuous measurement is determined, and simulation results are verified by laboratory experiment. On this basis, a radial basis function neural network and a combination model based on the grey system, genetic algorithm and neural work based wheel-rail force continuous measure method is put forward. It has been proved their high precision and feasibility through field test and reduces the influence of sensor failures on measure results.
(2) Research of wheel-rail force decouping
Study strain outputs of wheel-rail vertical force measure point under the action of lateral force and strain outputs of lateral force measure point under the action of vertical force by simulation, the results show that wheel-rail vertical force and lateral force exist crosstalk, at the same time, this phenomenon is found in laboratory test. Aim at this problem, this paper proposes to use an improved independent component analysis algorithm to decouple the crosstalk of wheel-rail vertical force and lateral force, this method provides a new approach for high precision wheel-rail force measurement.
(3) Establish of vehicle-track coupling dynamics model
On the basis of the theory of vehicle-track coupling dynamics, with high speed train as research object, railway vehicle dynamics simulation technology is used to systematically analyse wheel-rail contact and interaction of vehicle-track coupling system, a mechanical model of vehicle and track structure and dynamics simulation model are established respectively, the Hertz contact theory and Kalker rolling contact theory are employed to research wheel-rail interaction process and wheel-rail force of out-of-round wheel, which provides a basis for the study the influence of out-of-round wheel on wheel-rail dynamic characteristics.
(4) Analysis of the influence of out-of-round wheel on wheel-rail dynamic action
Out-of-round wheel is a serious hidden danger for vehicle running safety. This paper defines and classifies the forms of out-of-round wheel, combines with the established vehicle-track coupling dynamics model to study wheel-rail impact and vibration characteristic caused by different out-of-round wheel styles, such as wheel flats and wheel periodic polygon, which provides a theoretical basis for the establishment of wheel-rail force real-time monitoring system.
(5) Identification of out-of-round wheel through wheel-rail force
Vehicle-track coupling dynamics model is used to calculate wheel-rail impact force caused by80groups of wheel flat with different length and depth, and the wheels are divided into3styles according to the amplitude of wheel-rail force, there are normal wheels, fault wheels and severe fault wheels. Compress the original data through feature extraction firstly to avoid the curse of dimensionality, and then analyses three different classifiers:BP neural network, learning vector quantization neural network and support vector machine for wheel classification case in detail.
(6) Identification of out-of-round wheel through axle box vibration acceleration
Out-of-round wheel could cause abnormal vibration of the axle box, we could identify potential wheel defect through axle box vibration information. This paper uses measured data and simulation results, puts forward a Hilbert spectrum method based on improved EMD to qualitative identify out-of-round wheel effectively, the method can not only determine whether the wheel exists defect, but also judge the wheel defect is whether wheel flat or wheel polygon regardless of the speed. At the same time, quantitatively judge defect degree by means of wavelet package decomposition.
(1)轮轨力连续测试的实现
根据轮轨相互作用的特点和轨道的约束条件,通过仿真计算研究了轮轨垂向力与横向力测试问题,详细分析了荷载在移动过程中轨道的响应的变化以及不同位置测点的应变输出,确定了连续测试方案中测点的布置位置,并通过室内试验进行了验证。以此为依据,在现场测试中提出了一种基于径向基函数神经网络的轮轨力连续测试方法,以及一种基于灰色理论、遗传算法与神经网络的组合模型的轮轨力连续测试方法。通过现场试验,证明了其高精度与可行性,并降低了传感器误差对测试结果的影响。
(2)轮轨力解耦问题的研究
通过仿真计算研究了横向力作用下的垂向力测点的应变输出以及垂向力作用下的横向力测点的应变输出,发现横向力和垂向力间存在串扰问题,同时在室内试验中也发现了此现象。针对此问题,本文提出使用一种改进的独立分量分析算法对轮轨力的串扰进行解耦,为高精度轮轨力测试提供了一种新的手段。
(3)车辆轨道耦合动力学仿真分析模型的建立
以车辆轨道耦合动力学理论为基础,以高速列车为研究对象,采用铁道车辆动力学仿真技术,系统地分析车辆轨道耦合系统的轮轨接触和相互作用问题,分别建立车辆和轨道结构的力学模型和动力学仿真分析模型,采用Hertz接触理论和Kalker滚动接触理论,研究轮轨滚动接触关系中车轮失圆时的轮轨相互作用的过程和轮轨作用力,为研究车轮失圆对轮轨系统动力特征的影响提供基础。
(4)车轮失圆对轮轨动力作用的影响分析
车轮失圆是影响行车安全的重大隐患。对车轮失圆形式进行定义分类,结合所建立的车辆轨道耦合动力学仿真分析模型,研究车轮扁疤、车轮周期性多边形磨耗等常见的车轮失圆类型所造成的轮轨冲击振动的特征,从而为轮轨力实时监测系统的建立提供理论基础。
(5)基于轮轨力的车轮失圆的识别
利用车辆轨道耦合动力学仿真模型,计算了80组不同长度、不同深度的车轮扁疤作用下的轮轨冲击力,根据轮轨力的大小将车轮分为3种类型：正常车轮、问题车轮和严重问题车轮。首先通过特征提取方法对原始数据进行压缩,避免维数灾难,再详细分析了3种分类器：BP神经网络、学习向量量化神经网络和支持向量机对车轮进行分类识别情况。
(6)基于轴箱振动加速度的车轮失圆的识别
车轮失圆将会引起轴箱的异常振动,通过安装在轴箱上的加速度传感器可能对车轮失圆情况进行识别。本文提出一种基于改进的EMD的Hilbert谱方法,利用实测数据及仿真计算,有效地对车轮失圆问题进行了定性的识别,该方法不仅能判断车轮是否存在失圆状况,还能判断失圆类型是车轮扁疤还是车轮多边形化,且不受车速的影响。同时,利用小波包分解还能对失圆程度进行定量判断。
Real-time safety monitoring of train key component is an important means to ensure the security of train operation. Through the real-time recording, analysis and diagnosis, timely detect vehicles which exists serious safety hazard and find out the location of the fault and its reason, furthermore, implement of vehicle maintenance, which has a very positive role in the protection of railway traffic safety and significant social and economic benefits. This paper is in the context of the leaps and bounds development of the China railway, use the wheel-rail dynamic impact as the theme, from the point of view vehicle and track overall system, comprehensively research vehicle-track dynamic interaction security issue. This paper mainly carries out the following research works:
(1) Realization of wheel-rail force continuous measurement
According to wheel-rail interaction characteristics and the restriction condition of track, the single point measure of wheel-rail vertical force and lateral force are studied by means of simulation calculation, rail response change when load moves and stain outputs of measure points in different position are detailed analysed, finally the measure points arrangement position of continuous measurement is determined, and simulation results are verified by laboratory experiment. On this basis, a radial basis function neural network and a combination model based on the grey system, genetic algorithm and neural work based wheel-rail force continuous measure method is put forward. It has been proved their high precision and feasibility through field test and reduces the influence of sensor failures on measure results.
(2) Research of wheel-rail force decouping
Study strain outputs of wheel-rail vertical force measure point under the action of lateral force and strain outputs of lateral force measure point under the action of vertical force by simulation, the results show that wheel-rail vertical force and lateral force exist crosstalk, at the same time, this phenomenon is found in laboratory test. Aim at this problem, this paper proposes to use an improved independent component analysis algorithm to decouple the crosstalk of wheel-rail vertical force and lateral force, this method provides a new approach for high precision wheel-rail force measurement.
(3) Establish of vehicle-track coupling dynamics model
On the basis of the theory of vehicle-track coupling dynamics, with high speed train as research object, railway vehicle dynamics simulation technology is used to systematically analyse wheel-rail contact and interaction of vehicle-track coupling system, a mechanical model of vehicle and track structure and dynamics simulation model are established respectively, the Hertz contact theory and Kalker rolling contact theory are employed to research wheel-rail interaction process and wheel-rail force of out-of-round wheel, which provides a basis for the study the influence of out-of-round wheel on wheel-rail dynamic characteristics.
(4) Analysis of the influence of out-of-round wheel on wheel-rail dynamic action
Out-of-round wheel is a serious hidden danger for vehicle running safety. This paper defines and classifies the forms of out-of-round wheel, combines with the established vehicle-track coupling dynamics model to study wheel-rail impact and vibration characteristic caused by different out-of-round wheel styles, such as wheel flats and wheel periodic polygon, which provides a theoretical basis for the establishment of wheel-rail force real-time monitoring system.
(5) Identification of out-of-round wheel through wheel-rail force
Vehicle-track coupling dynamics model is used to calculate wheel-rail impact force caused by80groups of wheel flat with different length and depth, and the wheels are divided into3styles according to the amplitude of wheel-rail force, there are normal wheels, fault wheels and severe fault wheels. Compress the original data through feature extraction firstly to avoid the curse of dimensionality, and then analyses three different classifiers:BP neural network, learning vector quantization neural network and support vector machine for wheel classification case in detail.
(6) Identification of out-of-round wheel through axle box vibration acceleration
Out-of-round wheel could cause abnormal vibration of the axle box, we could identify potential wheel defect through axle box vibration information. This paper uses measured data and simulation results, puts forward a Hilbert spectrum method based on improved EMD to qualitative identify out-of-round wheel effectively, the method can not only determine whether the wheel exists defect, but also judge the wheel defect is whether wheel flat or wheel polygon regardless of the speed. At the same time, quantitatively judge defect degree by means of wavelet package decomposition.
引文
[1]沈志云.关于高速铁路及高速列车的研究.振动、测试与诊断.1998,18(1)：1-7
[2]金学松,沈志云.轮轨滚动接触疲劳研究的最新进展.铁道学报,2001,23(2)：92-108
[3]李玲.车轮多边形化对直线电机车辆动力学行为的影响.西南交通大学硕士学位论文,2010
[4]张兵.列车关键部件安全监测理论与分析研究.西南交通大学博士学位论文,2007
[5]R. Lagneback. Evaluation of wayside condition monitoring technologies for condition-based maintenance of railway vehicles. Doctoral dissertation of Lulea University of Technology,2007
[6]F. D. Irani. Development and deployment of advanced wayside condition monitoring. Foreign Rolling Stock,2002,39(2):39-43,45
[7]冯毅杰,张格明.车辆运行状态地面安全监测系统研究的新进展.中国铁道科学,2002,23(3)：138-142
[8]T. W. Moynihan, G. W. English. Railway safety technologies. Railway Safety Act Review Secretariat,2007
[9]C. Koniditsiotis. Weigh-in-motion technology. Austroads Incorporated,2000
[10]H. Tsunashima, Y. Naganuma, A.Matsumoto, et al. Japanese railway condition monitoring of tracks using in-service vehicle. Proceedings of the 5th IET International Conference on Railway Condition Monitoring and non-destructive testing,2011, 333-356.
[11]M. Bocciolone, A. Caprioli, A. Cigada, et al. A measurement system for quick rail inspection and effective track maintenance strategy. Mechanical System and Signal Processing,2007,21(3):1242-1254.
[12]A. Caprioli, A. Cigada, D. Raveglia. Rail inspection in track maintenance:a benchmark between the wavelet approach and the more conventional Fourier analysis. Mechanical System and Signal Processing,2007,21(2):631-652.
[13]J. Lundgren. Advanced rail vehicle inspection system. Foreign Rolling Stock, 2007,44(1):40-46
[14]王荣胜,陈岩,谢伟.TFDS的使用效果分析及改进措施.铁道车辆,2006,44(12)：38-40
[15]D. W. Barke, W. K. Chiu. Structural health monitoring in the railway industry:a review. Structural Health Monitoring,2005,4(1):81-94
[16]Federal Railroad Administration. Motive power and equipment compliance manual, 2010.
[17]S. M. Zakharov, I. Zharov. Criteria of bogie performance and wheel/rail wear prediction based on wayside measurements. Wear,2005,258(7-8):1135-1141
[18]徐其瑞,许建明,黎国清.轨道检查车技术的发展与应用.中国铁路,2005,(9)：37-39
[19]S. Ono, A. Numakura, T. Odaka. High-speed track inspection technologies. JR EAST Review,2003, (2):9-13
[20]王勤忠,曹勇健.试验构架(侧架)力的双桥解耦求导.铁道车辆,2000,38(9)：20-21,27
[21]H. Kanehara, T. Fujioka. Measuring rail/wheel contact points of running railway vehicles. Wear,2002,253(1-2):278-283
[22]陈建政.轮轨作用力和接触点位置在线测量理论研究.西南交通大学博士学位论文,2008
[23]J. J. Zhu, A. K. W. Ahmed, S. Rakhejia, et al. Impact load due to railway wheels with multiple flats predicted using an adaptive contact model. Proceedings of the Institution of Mechanical Engineers, Part F, Journal of Rail and Rapid Transit,2009, 233(4):391-403
[24]R. U. A. Uzzal, A. K. W. Ahmed, S. Rakhejia. Analysis of pitch plane railway vehicle-track interactions due to single and multiple wheel flats. Proceedings of the Institution of Mechanical Engineers, Part F, Journal of Rail and Rapid Transit,2009, 233(4):375-390
[25]李彬,林建辉.基于轨道轮轨力连续测试的车辆运行状态地面安全监测系统的研究.实用测试技术,2002,(5)：9-10
[26]刘继,冯铭.车轮踏面擦伤在线自动检测方法的研究和试验.铁道车辆,1995,33(2)：28-31
[27]洪溢飏.地面连续测量轮轨力方法研究.西南交通大学硕士学位论文,2012
[28]曾宇清,张格明,张岩,等.一种基于钢轨应变的轮轨垂直力连续测量方法及装置.CN 101571432A,2006
[29]赵国堂,田越,刘铁,等.轮轨水平力连续测试技术的研究.铁道学报,2000,22(3)：69-73
[30]A. Matsumoto, Y. Sato, H. Ohno, et al. A new monitoring method of train derailment coefficient. Proceedings of the 3th ITE International Conferrence on Railway Condition Monitoring,2006:136-140
[31]潘周平,张立民.轮轨力连续测试系统设计.西南交通大学学报,2004,39(1)：69-72
[32]农汉彪.轮轨垂向载荷连续测量与识别方法研究.西南交通大学博士学位论文,2012
[33]宋颖,杜彦良,孙宝臣.压电传感技术在轮轨力实时监测中的应用探讨.振动与冲击,2010,29(1)：228-232
[34]C. Delprete, C. Rosso. An easy instrument and a methodology for the monitoring and the diagnosis of a rail. Mechanical System and Signal Processing,2009,23(3):940-956
[35]M. L. Filograno, P. Corredera, A.R-Barrios, et al. Real time monitoring of railway traffic using fiber Bragg grating sensors. Sensors Journal,2012,12(1):85-92
[36]T. K. Ho, S. Y. Liu, K. H. Ho, et al. Signature analysis on wheel-rail interaction for rail defect detection. Proceedings of the 4th IET International Conference on Railway Condition Monitoring,2008,1-6
[37]S. G. Newton, R. A. Clark. An investigation into the dynamic effects on the track of wheelflats on railway vehicles. Journal of Mechanics Engineering Science,1979,21(4): 287-297
[38]D. R. Ahlbeck. A study of dynamic impact load effects due to railroad wheel profile roughness. In Proceedings of 10th IAVSD Symposium, Prague, CSSR, August 24-28 1987,13-16
[39]D. R. Ahlbeck, J. A. Hadden. Measurement and prediction of impact loads from worn railroad wheel and rail surface profiles. Trans. ASME, J. Engng for Industry,1985, (107): 197-205
[40]J. C. O. Nielsen, A. Johansson. Out-of-round railway wheels-a literature survey. Journal of Rail & Rapid Transit,2000, (214):79-91
[41]H. Jenkins, H. Stephenson. The effect of track and vehiele parameters on wheel/rail vertical dynamic forces. Railway Engng,1974:2-16
[42]Z. Cai, G. P. Raymond. Modelling the dynamic reponse of railway track to wheel/rail impact loading. Structural Engng and Mechanics,1994,2(1):95-121
[43]R. G. Dong, S. Sankar. The characteristics of impact loads due to wheel tread defects. RTD rail Transpn. ASME,1994, (8):23-30
[44]J. Kalousek, K. L. Johnson. An investigation of short pitch wheel and rail corrugations on the Vancouver mass transit system. Proceedings of the Institution of Mechanical Engineers, Part F, Journal of Rail and Rapid Transit,1992, (206):127-135
[45]B. Soua, J. P. Pascal. Computation of the 3D wear of the wheels in a high speed bogie. Report INRETS-LIN, Arcueil, France.
[46]A. Johansson, C. Andersson. Out-of-round railway wheels-a study of wheel polygonalization through simulation of three-dimensional wheel-rail interaction and wear. Vehicle System Dynamics,2005(43):539-559
[47]A. Johansson, J. C. O. Nielsen. Out-of-round railway wheels-wheel-rail contact forces and track response derived from field tests and numerical simulations. Rail and Rapid Transit,2003,217(2):135-146
[48]H. Mok, W. K. Chiu, D. Peng, et al. Rail wheel removal and its implication on track life: a fracture mechanics approach,2007,48(1):21-31.
[49]J. Jergeus, C. Odenmarck, R. Lunden, et al. Full-scale railway wheel flat experiment. Proceedings of the Institution of Mechanical Engineers, Part F, Journal of Rail and Rapid Transit,1999,213(1):1-13
[50]刘建新,易明辉,王开云.重载铁路车轮踏面擦伤时的轮轨动态相互作用特征.交通运输工程学报,2010,10(3)：52-56
[51]张雪珊,肖新标,金学松.高速车轮椭圆化问题及其对车辆横向稳定性的影响.机械工程学报,2008,44(3)：50-56
[52]宋颖.高速车轮失圆对轮轨动力作用的影响及其监测方法研究.北京交通大学博士学位论文,2010
[53]D. H. Stone, S. F. Kalay, A. Tajaddini. Statistical behaviour of wheel impact load detectors to various wheel defects.10th International Wheelset Congress,1992,9-13
[54]A. M. Remennikov, S. Kaewunruen. A review of loading conditions for railway track structures due to train and track vertical interaction. Structural Control and Health Monitoring,2008,15(2):207-234
[55]T. X. Wu, D. J. Thompson. Vibration analysis of railway track with multiple wheels on the rail. Journal of Sound and Vibration,2001,239(1):69-97
[56]V. Belotti, F. Crenna, R. C. Michelini, et al. Wheel-flat diagnostic tool via wavelet transform. Mechanical System and Signal Processing,2006, (20):1953-1966.
[57]T. Ohtani. Development of a wheel-flat detection system.11th International Wheelset Conference,1995,235-240.
[58]M. Ogasawara. Development of trackside rolling stock monitoring system. Foreign Locomotive & Rolling Stock Technology,2000, (5):1-3,14
[59]A. Bracciali, G. Cascini. Detection of corrugation and wheelflats of railway wheels using energy and cepstrum analysis of rail acceleration. Proceedings of the Institution of Mechanical Engineers, Part F, Journal of Rail and Rapid Transit,1997,211(2):109-116
[60]王祯,郭建强,高晓蓉,等.火车车轮踏面缺陷多通道检测系统的研究.光电工程,2011,38(7)：92-98
[61]岳建海,裘正定,李铁锚.基于连续子波变换的铁路车轮踏面擦伤的在线检测.铁道学报,2003,25(4)：27-30
[62]葛林富,尹治本,朱怀芳.机车踏面擦伤计算机智能检测原理及实现.铁道学报,1993,15(2)：35-39
[63]冯其波,赵雁,崔建英.车轮踏面擦伤动态定量测量新方法.机械工程学报,2002,38(2)：120-122
[64]王雪,谢歆,赵国华.基于小波反弹神经网络的车轮擦伤检测方法.中国机械工程,2003,14(20)：1783-1785
[65]李景泉,刘继.车轮踏面擦伤自动检测方法的研究和试验.同济大学学报,2003,31(4)：473-476
[66]杨振祥.车轮踏面磨耗和擦伤在线自动检测诊断系统探讨.机车车辆工艺,1998,(1)：42-45
[67]敖银辉,徐晓东,吴乃优.用激光位移传感器检测轮对踏面缺陷.西南交通大学学报,2009,39(3)：346-348
[68]P. L. Gutauskas, Markham. Railroad flat wheel detectors. PN5133521J992
[69]I. Octavian. Wheel profile automatic detection system. Foreign Rolling Stock,1995, (4):53-57
[70]田丽丽,方宗德,赵勇.铁路货车车轮踏面损伤检测中剥离与擦伤定位方法.铁道学报,2009,31(5)：31-36
[71]赵勇.基于GA-RBFNN算法的列车车轮踏面损伤识别.计算机工程与应用,2012,48(8)：32-34
[72]J. Brizuela, A. Ibanez, P. Nevado, et al. Railway wheels flat detector using Doppler effect. Physics Procedia,2010,(3):811-817
[73]J. Brizuela, A. Ibanez, C. Fritsch. NDE system for railway wheel inspection in a standard FPGA. Journal of Systems Architecture,2010,(56):616-622
[74]R. Pohl, A. Erhard, H. J. Montag, et al. NDT techniques for railroad wheel and gauge corner inspection. NDT&E International,2004,(37):89-94
[75]N. A. Thakkar, J. A. Steel, R. L. Reuben. Rail-wheel contact stress assessment using acoustic emission:a laboratory study of the effects of wheel flats. Proceedings of the Institution of Mechanical Engineers, Part F, Journal of Rail and Rapid Transit,2012, 226(1):3-13
[76]K. Bollas, D. Papasalouros, D. Kourousis. Acoustic emission inspection of rail wheels. Journal of Acoustic Emission,2010,(28):215-228
[77]王炎孝,杨占平.车轮扁疤动态检测方法综述.铁道车辆,2002,40(6)：9-12
[78]C. Zang, M. Imregun. Structural damage detection using artificial neural networks and measured FRF data reduced via principal component projection. Journal of Sound and Vibration,2001,242(5):813-827
[79]S. Haran, R. D. Finch. Application of an automated package of pattern recognition techniques to acoustic signature inspection of railroad wheels. Journal of Acoustical Society of America,1988,85(1):440-449
[80]王雪,付振波.采用小波分析与支持向量机的车轮踏面擦伤识别方法.中国机械工程,2004,15(18)：1641-1643
[81]姜爱国,王雪.车轮踏面擦伤的集成粗糙神经网络预示诊断.清华大学学报,2005,45(2)：170-173
[82]魏志刚,程建政,褚梅娟.模糊模式识别在轮箍超声横波探伤中的应用.声学技术,2006,25(5)：426-430
[83]M. Molodova, Z. Li, R. Dollevoet. Axle box acceleration:measurement and simulation for detection of short track defects. Wear,2011,271:349-356
[84]Z. Li, M. Molodova, R. Dollevoet. An investigation of the possibility to use axle box acceleration for condition monitoring of welds. International conference on noise and vibration engineering,2008.
[85]Y. Q. Sun, C. Cole, M. Mcclanachan, et al. Rail short-wavelength irregularity identification based on wheel-rail impact response measurement and simulations. International Heavy Haul Conference,2009,210-218
[86]林建辉,陈建政.基于神经网络的车辆轴箱谱自适应解耦研究.机械科学与技术,1999,18(3)：486-488
[87]金学松,张雪珊,张剑,等.轮轨关系研究中的力学问题.机械强度,2005,27(4)：408-418
[88]练松良.轨道工程.人民交通出版社,2009
[89]S. Lu. Real-time vertical track deflection measurement system. Doctoral dissertation of university of Nebraska,2008
[90]佐藤吉彦.新轨道力学.中国铁道出版社,2001
[91]曾树谷.铁路轨道动力测试技术.中国铁道出版社,1988
[92]李德葆.振动测试与应变电测基础.清华大学出版社,1987
[93]B. Stratman, Y. M. Liu, S. Mahadevan. Structural health monitoring of railroad wheels using wheel impact load detectors. Journal of Failure Analysis and Prevention,2007, 7(3):218-225
[94]J. Jonsson, E. Svensson, J. T. Christensen. Strain gauge measurement of wheel-rail interaction forces. Journal of Strain Analysis,1997,32(3):183-191
[95]翟婉明.车辆轨道耦合动力学.科学出版社,2007
[96]P. Gullers, L. Anderssona, R. Lunden. High-frequency vertical wheel-rail contact forces-Field measurements and influence of track irregularities. Wear,2008, (265):1472-1478
[97]J. C. O. Nielsen. High-frequency vertical wheel-rail contact forces-Validation of a prediction model by field testing. Wear,2008, (265):1465-1471
[98]J. H. Holland. Adaptation in natural and artificial systems. The university of Michigan press,1975
[99]雷英杰Matlab遗传算法工具箱及应用.西安电子科技大学出版社,2005
[100]陈根社,陈新海.遗传算法的研究与进展.信息与控制,1994,23(4)：215-222
[101]N. Nencho, R. Tome, M. Georgy, et al. Strength sensor for dynamic wheel load measuring of railway carriages.26th International Spring Seminar on Electronics Technology,2003,260-265.
[102]潘立登,吴宁川.径向基函数神经网络正交最小二乘改进算法的实现.北京化工大学学报,2002,29(4)：82-84
[103]邓聚龙.灰色控制系统.华中科技大学出版社,1993
[104]张怡,魏勇,熊常伟.灰色模型GM(1,1)的一种新优化方法.系统工程理论与实践,2007,(4)：141-146
[105]刘思峰.灰色系统理论及其应用(第五版).科学出版社,2010,146-168
[106]C. C. Hsu, C. Y. Chen. Applications of improved grey prediction model for power demand forecasting. Energy Conversion and Management,2003, (44):2241-2249
[107]付继华,孟浩,王中宇.基于灰色理论的动态测量系统非统计建模方法.仪器仪表学报,2008,29(6)：1245-1249
[108]张大海,江世芳,史开泉.灰色预测公式的理论缺陷及改进.系统工程理论与实践,2002,22(8)：140-142
[109]吉培荣,黄巍松,胡翔勇.灰色预测模型特性的研究.系统工程理论与实践,2001,21(9)：105-109
[110]林晓言,陈有孝.基于灰色-马尔科大链改进方法的铁路货运量预测研究.铁道学报,2005,27(3)：15-19
[111]C. H. Wang. Predicting tourism demand using fuzzy times series and hybrid grey theory. Tourism Management,2004,25(3):367-374
[112]周铭,王洪发.基于遗传算法的灰色GM(1,1)模型.南昌大学学报(理科版),2002,12(4)：331-333
[113]L. C. Hsu. Forecasting the output of integrated circuit industry using genetic algorithm based multivariable grey optimization models. Expert System with Applications,2009, 36(4):7898-7903
[114]袁景凌,李小燕,钟珞.遗传优化的灰色神经网络模型比较研究.计算机工程与应用,2010,46(2)：41-43
[115]陈淑燕,王炜.交通量的灰色神经网络预测方法.东南大学学报(自然科学版),2004,34(4)：541-544
[116]T. Y. Pai, Y. P. Tsai, H. M. Lo, et al. Grey and network prediction of suspended solids and chemical oxygen demand in hospital wastewater treatment plant effluent. Computer and Chemical Engineering,2007,31(10):1272-1281.
[117]曹建华,刘渊,代悦.一种基于灰色神经网络的网络流量预测模型.计算机工程与应用,2008,44(5)：155-157.
[118]万星,周建中.改进灰色神经网络模型在电量预测中的应用.水力发电,2007,33(6)：69-72
[119]党耀国,刘思峰,刘斌.以x(1)(N)为初始条件的GM模型.中国管理科学,2005,13(1)：132-135.
[120]任愈,陈建政,林建辉.测力轮对轮轨力检测盲信号分离方案研究.机械科学与技术,2010,29(3)：289-292
[121]C. Jutten, J. Herault. Blind separation of sources, part I:an adaptive algorithm based on neuromimetic architecture. Signal Processing,1991,28:1-10
[122]史习智.盲信号处理-理论与实践.上海交通大学出版社,2008
123] Y. Li, A. Cichocki, S. I. Amari. Sparse component analysis for blind source separation with less sensors than sources. Proceedings of ICA,2003
124] A. Hyvarinen. Fast and robust fixed-point algorithm for independent component analysis. IEEE Trans. On Neural Network,1999,10(3):626-634
125] A. Hyvarinen. E. Oja. Independent component analysis by general nonlinear Hebbian like learning rules. Signal Processing,1998,64(3):301-313
126]姚志湘,粟晖,刘焕彬.针对FastICA计算终点判断的算法改进.计算机工程与应用,2006,42(26)：46-49
127] Z. X. Yao, K. Zhang, H. B. Liu, et al. Eliminate indeterminacies of independent component analysis for chemometrics. Progress in Natural Science,2008, 18(8):1009-1014
128]汪斌,王年,蒋云志,等.改进FastICA算法在谐波检测中的应用.电力自动化设备,2011,31(3)：135-138
129]姚志湘,刘焕彬,粟晖.盲信号分离输出与源信号的一致性判断.华南理工大学学报(自然科学版),2007,35(5)：50-53
130]何昭水,谢胜利,傅予力.稀疏表示与病态混叠盲分离.中国科学E辑：信息科学,2006,36(8)：864-879
131] A. Johansson. Out-of-round railway wheels-assessment of wheel tread irregularities in train traffic. Journal of Sound and Vibration,2006, (293):795-806
132] T. Mazilu. A dynamic model for the impact between the wheel flat and rail. Mechanical Engineering,2007,69(2):45-58
133] M. M. Steenbergen. Wheel-rail interaction at short-wave irregularities. Doctoral dissertation of technology university of Delft,2008
134] D. Lyon. The calculation of track forces due to dipped rail joints, wheel flats and rail weld. The second ORE colloquium on technical computer programs,1972
135]翟婉明.铁路车轮扁疤的动力学效应.铁道车辆,1994,(7)：1-5
136] Wheel impact detection systems-the North American experience. Zeta-Tech Associates, Suite Cherry Hill,1997
137] J. M. Samuels, J. J. Palesano. Using mainline wheel impact detectors to increase the productivity of car inspection functions. Mechanical Engineering Transaction, Institution of Engineers,1988, (3):165-168
138] A. Tajaddini, S. F. Kalay. Time to revise wheel-removal ruels. Railway Age,1995, 196(9):93-98
139] AAR. Association of American railroad rule 41-section A,2002
140]蒋宗礼.人工神经网络导论.高等教育出版社,2001
141] T. Kohonen. Self-organization and associative memory. Springer-Verlag,1987
142]邓乃扬,田英杰.支持向量机-理论、算法与拓展.科学出版社,2009
143] U. Thissen, R. Brakel, A. P. Weijer, et al. Using support vector machines for time series prediction. Chemometrics and Intelligent Laboratory Systems,2003,69(1-2):35-49
[144]L. J. Herrera, H. Pomares, I. Rojas, et al. Recursive prediction for long term time series forecasting using advanced models. Neurocomputing,2007,70(16-18):2870-2880
[145]陈果.基于遗传算法的支持向量机时间序列预测模型优化.仪器仪表学报,2006,27(9)：1080-1084
[146]陈果.基于遗传算法的支持向量机分类器模型参数优化.机械科学与技术,2007,26(3)：347-350
[147]E. Pourbasheer, S. Riahi, M. R. Ganjali, et al. Application of genetic algorithm support vector machine (GA-SVM) for prediction of BK-channels activity. European Journal of Medicinal Chemistry,2009,44(12):5023-5028
[148]B. Samanta, K. R. Al-Balushi, S. A. Al-Araimi. Artificial neural networks and support vector machine with genetic algorithm for bearing fault detection. Engineering Application of Artificial Intelligence,2003,16(7-8):657-665
[149]R. Karoumi, J. Wiberg, A. Liljencrantz. Monitoring traffic loads and dynamic effects using an instrumented railway bridge. Engineering Structures,2005,27(12):1813-1819
[150]N. E. Huang, Z. Shen, S. R. Long. The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis. Proceedings of the Royal Society of London,1998,454(1971):903-995
[151]ZHAO Jinping, HUANG Daji. Mirror extending and circular spline function for empirical mode decomposition method. Journal of Zhejiang University (Science),2001, 2(3):247-252
[152]DENG Yongjun, WANG Wei, QIAN Chengchun, et al. Boundary-processing-technique in EMD method and Hilbert transform. Chinese Science Bulletin,2001,46(11):954-961
[153]刘慧婷,张旻,程家兴.基于多项式拟合算法的EMD端点问题的处理.计算机工程与应用,2004,40(16)：84-86,100
[154]张郁山,梁建文,胡聿贤.应用自回归模型处理EMD方法中的边界问题.自然科学进展,2003,13(10)：1054-1059
[155]CHENG Junsheng, YU Dejie, YANG Yu. Application of support vector regression machines to the processing of end effects of Hilbert-Huang transform. Mechanical Systems and Signal Processing,2007,21(3):1197-1211
[156]邵晨曦,王剑,范金锋,等.一种自适应的EMD端点延拓方法.电子学报,2007,35(10)：1944-1948
[157]陈双喜,林建辉,陈建政.基于改进的EMD方法提取车辆-轨道垂向耦合系统动态特征.振动与冲击,2011,30(8)：212-216
[158]何正嘉.现代信号处理及工程应用.西安交通大学出版社,2007
[159]S. G. Mallat. A wavelet tour of signal processing. Academic Press,1999
[160]杨国安,钟秉林,黄仁,等.机械故障信号小波包分解的时域特征提取方法研究.振动与冲击,2001,20(2)：25-28,31
[2]金学松,沈志云.轮轨滚动接触疲劳研究的最新进展.铁道学报,2001,23(2)：92-108
[3]李玲.车轮多边形化对直线电机车辆动力学行为的影响.西南交通大学硕士学位论文,2010
[4]张兵.列车关键部件安全监测理论与分析研究.西南交通大学博士学位论文,2007
[5]R. Lagneback. Evaluation of wayside condition monitoring technologies for condition-based maintenance of railway vehicles. Doctoral dissertation of Lulea University of Technology,2007
[6]F. D. Irani. Development and deployment of advanced wayside condition monitoring. Foreign Rolling Stock,2002,39(2):39-43,45
[7]冯毅杰,张格明.车辆运行状态地面安全监测系统研究的新进展.中国铁道科学,2002,23(3)：138-142
[8]T. W. Moynihan, G. W. English. Railway safety technologies. Railway Safety Act Review Secretariat,2007
[9]C. Koniditsiotis. Weigh-in-motion technology. Austroads Incorporated,2000
[10]H. Tsunashima, Y. Naganuma, A.Matsumoto, et al. Japanese railway condition monitoring of tracks using in-service vehicle. Proceedings of the 5th IET International Conference on Railway Condition Monitoring and non-destructive testing,2011, 333-356.
[11]M. Bocciolone, A. Caprioli, A. Cigada, et al. A measurement system for quick rail inspection and effective track maintenance strategy. Mechanical System and Signal Processing,2007,21(3):1242-1254.
[12]A. Caprioli, A. Cigada, D. Raveglia. Rail inspection in track maintenance:a benchmark between the wavelet approach and the more conventional Fourier analysis. Mechanical System and Signal Processing,2007,21(2):631-652.
[13]J. Lundgren. Advanced rail vehicle inspection system. Foreign Rolling Stock, 2007,44(1):40-46
[14]王荣胜,陈岩,谢伟.TFDS的使用效果分析及改进措施.铁道车辆,2006,44(12)：38-40
[15]D. W. Barke, W. K. Chiu. Structural health monitoring in the railway industry:a review. Structural Health Monitoring,2005,4(1):81-94
[16]Federal Railroad Administration. Motive power and equipment compliance manual, 2010.
[17]S. M. Zakharov, I. Zharov. Criteria of bogie performance and wheel/rail wear prediction based on wayside measurements. Wear,2005,258(7-8):1135-1141
[18]徐其瑞,许建明,黎国清.轨道检查车技术的发展与应用.中国铁路,2005,(9)：37-39
[19]S. Ono, A. Numakura, T. Odaka. High-speed track inspection technologies. JR EAST Review,2003, (2):9-13
[20]王勤忠,曹勇健.试验构架(侧架)力的双桥解耦求导.铁道车辆,2000,38(9)：20-21,27
[21]H. Kanehara, T. Fujioka. Measuring rail/wheel contact points of running railway vehicles. Wear,2002,253(1-2):278-283
[22]陈建政.轮轨作用力和接触点位置在线测量理论研究.西南交通大学博士学位论文,2008
[23]J. J. Zhu, A. K. W. Ahmed, S. Rakhejia, et al. Impact load due to railway wheels with multiple flats predicted using an adaptive contact model. Proceedings of the Institution of Mechanical Engineers, Part F, Journal of Rail and Rapid Transit,2009, 233(4):391-403
[24]R. U. A. Uzzal, A. K. W. Ahmed, S. Rakhejia. Analysis of pitch plane railway vehicle-track interactions due to single and multiple wheel flats. Proceedings of the Institution of Mechanical Engineers, Part F, Journal of Rail and Rapid Transit,2009, 233(4):375-390
[25]李彬,林建辉.基于轨道轮轨力连续测试的车辆运行状态地面安全监测系统的研究.实用测试技术,2002,(5)：9-10
[26]刘继,冯铭.车轮踏面擦伤在线自动检测方法的研究和试验.铁道车辆,1995,33(2)：28-31
[27]洪溢飏.地面连续测量轮轨力方法研究.西南交通大学硕士学位论文,2012
[28]曾宇清,张格明,张岩,等.一种基于钢轨应变的轮轨垂直力连续测量方法及装置.CN 101571432A,2006
[29]赵国堂,田越,刘铁,等.轮轨水平力连续测试技术的研究.铁道学报,2000,22(3)：69-73
[30]A. Matsumoto, Y. Sato, H. Ohno, et al. A new monitoring method of train derailment coefficient. Proceedings of the 3th ITE International Conferrence on Railway Condition Monitoring,2006:136-140
[31]潘周平,张立民.轮轨力连续测试系统设计.西南交通大学学报,2004,39(1)：69-72
[32]农汉彪.轮轨垂向载荷连续测量与识别方法研究.西南交通大学博士学位论文,2012
[33]宋颖,杜彦良,孙宝臣.压电传感技术在轮轨力实时监测中的应用探讨.振动与冲击,2010,29(1)：228-232
[34]C. Delprete, C. Rosso. An easy instrument and a methodology for the monitoring and the diagnosis of a rail. Mechanical System and Signal Processing,2009,23(3):940-956
[35]M. L. Filograno, P. Corredera, A.R-Barrios, et al. Real time monitoring of railway traffic using fiber Bragg grating sensors. Sensors Journal,2012,12(1):85-92
[36]T. K. Ho, S. Y. Liu, K. H. Ho, et al. Signature analysis on wheel-rail interaction for rail defect detection. Proceedings of the 4th IET International Conference on Railway Condition Monitoring,2008,1-6
[37]S. G. Newton, R. A. Clark. An investigation into the dynamic effects on the track of wheelflats on railway vehicles. Journal of Mechanics Engineering Science,1979,21(4): 287-297
[38]D. R. Ahlbeck. A study of dynamic impact load effects due to railroad wheel profile roughness. In Proceedings of 10th IAVSD Symposium, Prague, CSSR, August 24-28 1987,13-16
[39]D. R. Ahlbeck, J. A. Hadden. Measurement and prediction of impact loads from worn railroad wheel and rail surface profiles. Trans. ASME, J. Engng for Industry,1985, (107): 197-205
[40]J. C. O. Nielsen, A. Johansson. Out-of-round railway wheels-a literature survey. Journal of Rail & Rapid Transit,2000, (214):79-91
[41]H. Jenkins, H. Stephenson. The effect of track and vehiele parameters on wheel/rail vertical dynamic forces. Railway Engng,1974:2-16
[42]Z. Cai, G. P. Raymond. Modelling the dynamic reponse of railway track to wheel/rail impact loading. Structural Engng and Mechanics,1994,2(1):95-121
[43]R. G. Dong, S. Sankar. The characteristics of impact loads due to wheel tread defects. RTD rail Transpn. ASME,1994, (8):23-30
[44]J. Kalousek, K. L. Johnson. An investigation of short pitch wheel and rail corrugations on the Vancouver mass transit system. Proceedings of the Institution of Mechanical Engineers, Part F, Journal of Rail and Rapid Transit,1992, (206):127-135
[45]B. Soua, J. P. Pascal. Computation of the 3D wear of the wheels in a high speed bogie. Report INRETS-LIN, Arcueil, France.
[46]A. Johansson, C. Andersson. Out-of-round railway wheels-a study of wheel polygonalization through simulation of three-dimensional wheel-rail interaction and wear. Vehicle System Dynamics,2005(43):539-559
[47]A. Johansson, J. C. O. Nielsen. Out-of-round railway wheels-wheel-rail contact forces and track response derived from field tests and numerical simulations. Rail and Rapid Transit,2003,217(2):135-146
[48]H. Mok, W. K. Chiu, D. Peng, et al. Rail wheel removal and its implication on track life: a fracture mechanics approach,2007,48(1):21-31.
[49]J. Jergeus, C. Odenmarck, R. Lunden, et al. Full-scale railway wheel flat experiment. Proceedings of the Institution of Mechanical Engineers, Part F, Journal of Rail and Rapid Transit,1999,213(1):1-13
[50]刘建新,易明辉,王开云.重载铁路车轮踏面擦伤时的轮轨动态相互作用特征.交通运输工程学报,2010,10(3)：52-56
[51]张雪珊,肖新标,金学松.高速车轮椭圆化问题及其对车辆横向稳定性的影响.机械工程学报,2008,44(3)：50-56
[52]宋颖.高速车轮失圆对轮轨动力作用的影响及其监测方法研究.北京交通大学博士学位论文,2010
[53]D. H. Stone, S. F. Kalay, A. Tajaddini. Statistical behaviour of wheel impact load detectors to various wheel defects.10th International Wheelset Congress,1992,9-13
[54]A. M. Remennikov, S. Kaewunruen. A review of loading conditions for railway track structures due to train and track vertical interaction. Structural Control and Health Monitoring,2008,15(2):207-234
[55]T. X. Wu, D. J. Thompson. Vibration analysis of railway track with multiple wheels on the rail. Journal of Sound and Vibration,2001,239(1):69-97
[56]V. Belotti, F. Crenna, R. C. Michelini, et al. Wheel-flat diagnostic tool via wavelet transform. Mechanical System and Signal Processing,2006, (20):1953-1966.
[57]T. Ohtani. Development of a wheel-flat detection system.11th International Wheelset Conference,1995,235-240.
[58]M. Ogasawara. Development of trackside rolling stock monitoring system. Foreign Locomotive & Rolling Stock Technology,2000, (5):1-3,14
[59]A. Bracciali, G. Cascini. Detection of corrugation and wheelflats of railway wheels using energy and cepstrum analysis of rail acceleration. Proceedings of the Institution of Mechanical Engineers, Part F, Journal of Rail and Rapid Transit,1997,211(2):109-116
[60]王祯,郭建强,高晓蓉,等.火车车轮踏面缺陷多通道检测系统的研究.光电工程,2011,38(7)：92-98
[61]岳建海,裘正定,李铁锚.基于连续子波变换的铁路车轮踏面擦伤的在线检测.铁道学报,2003,25(4)：27-30
[62]葛林富,尹治本,朱怀芳.机车踏面擦伤计算机智能检测原理及实现.铁道学报,1993,15(2)：35-39
[63]冯其波,赵雁,崔建英.车轮踏面擦伤动态定量测量新方法.机械工程学报,2002,38(2)：120-122
[64]王雪,谢歆,赵国华.基于小波反弹神经网络的车轮擦伤检测方法.中国机械工程,2003,14(20)：1783-1785
[65]李景泉,刘继.车轮踏面擦伤自动检测方法的研究和试验.同济大学学报,2003,31(4)：473-476
[66]杨振祥.车轮踏面磨耗和擦伤在线自动检测诊断系统探讨.机车车辆工艺,1998,(1)：42-45
[67]敖银辉,徐晓东,吴乃优.用激光位移传感器检测轮对踏面缺陷.西南交通大学学报,2009,39(3)：346-348
[68]P. L. Gutauskas, Markham. Railroad flat wheel detectors. PN5133521J992
[69]I. Octavian. Wheel profile automatic detection system. Foreign Rolling Stock,1995, (4):53-57
[70]田丽丽,方宗德,赵勇.铁路货车车轮踏面损伤检测中剥离与擦伤定位方法.铁道学报,2009,31(5)：31-36
[71]赵勇.基于GA-RBFNN算法的列车车轮踏面损伤识别.计算机工程与应用,2012,48(8)：32-34
[72]J. Brizuela, A. Ibanez, P. Nevado, et al. Railway wheels flat detector using Doppler effect. Physics Procedia,2010,(3):811-817
[73]J. Brizuela, A. Ibanez, C. Fritsch. NDE system for railway wheel inspection in a standard FPGA. Journal of Systems Architecture,2010,(56):616-622
[74]R. Pohl, A. Erhard, H. J. Montag, et al. NDT techniques for railroad wheel and gauge corner inspection. NDT&E International,2004,(37):89-94
[75]N. A. Thakkar, J. A. Steel, R. L. Reuben. Rail-wheel contact stress assessment using acoustic emission:a laboratory study of the effects of wheel flats. Proceedings of the Institution of Mechanical Engineers, Part F, Journal of Rail and Rapid Transit,2012, 226(1):3-13
[76]K. Bollas, D. Papasalouros, D. Kourousis. Acoustic emission inspection of rail wheels. Journal of Acoustic Emission,2010,(28):215-228
[77]王炎孝,杨占平.车轮扁疤动态检测方法综述.铁道车辆,2002,40(6)：9-12
[78]C. Zang, M. Imregun. Structural damage detection using artificial neural networks and measured FRF data reduced via principal component projection. Journal of Sound and Vibration,2001,242(5):813-827
[79]S. Haran, R. D. Finch. Application of an automated package of pattern recognition techniques to acoustic signature inspection of railroad wheels. Journal of Acoustical Society of America,1988,85(1):440-449
[80]王雪,付振波.采用小波分析与支持向量机的车轮踏面擦伤识别方法.中国机械工程,2004,15(18)：1641-1643
[81]姜爱国,王雪.车轮踏面擦伤的集成粗糙神经网络预示诊断.清华大学学报,2005,45(2)：170-173
[82]魏志刚,程建政,褚梅娟.模糊模式识别在轮箍超声横波探伤中的应用.声学技术,2006,25(5)：426-430
[83]M. Molodova, Z. Li, R. Dollevoet. Axle box acceleration:measurement and simulation for detection of short track defects. Wear,2011,271:349-356
[84]Z. Li, M. Molodova, R. Dollevoet. An investigation of the possibility to use axle box acceleration for condition monitoring of welds. International conference on noise and vibration engineering,2008.
[85]Y. Q. Sun, C. Cole, M. Mcclanachan, et al. Rail short-wavelength irregularity identification based on wheel-rail impact response measurement and simulations. International Heavy Haul Conference,2009,210-218
[86]林建辉,陈建政.基于神经网络的车辆轴箱谱自适应解耦研究.机械科学与技术,1999,18(3)：486-488
[87]金学松,张雪珊,张剑,等.轮轨关系研究中的力学问题.机械强度,2005,27(4)：408-418
[88]练松良.轨道工程.人民交通出版社,2009
[89]S. Lu. Real-time vertical track deflection measurement system. Doctoral dissertation of university of Nebraska,2008
[90]佐藤吉彦.新轨道力学.中国铁道出版社,2001
[91]曾树谷.铁路轨道动力测试技术.中国铁道出版社,1988
[92]李德葆.振动测试与应变电测基础.清华大学出版社,1987
[93]B. Stratman, Y. M. Liu, S. Mahadevan. Structural health monitoring of railroad wheels using wheel impact load detectors. Journal of Failure Analysis and Prevention,2007, 7(3):218-225
[94]J. Jonsson, E. Svensson, J. T. Christensen. Strain gauge measurement of wheel-rail interaction forces. Journal of Strain Analysis,1997,32(3):183-191
[95]翟婉明.车辆轨道耦合动力学.科学出版社,2007
[96]P. Gullers, L. Anderssona, R. Lunden. High-frequency vertical wheel-rail contact forces-Field measurements and influence of track irregularities. Wear,2008, (265):1472-1478
[97]J. C. O. Nielsen. High-frequency vertical wheel-rail contact forces-Validation of a prediction model by field testing. Wear,2008, (265):1465-1471
[98]J. H. Holland. Adaptation in natural and artificial systems. The university of Michigan press,1975
[99]雷英杰Matlab遗传算法工具箱及应用.西安电子科技大学出版社,2005
[100]陈根社,陈新海.遗传算法的研究与进展.信息与控制,1994,23(4)：215-222
[101]N. Nencho, R. Tome, M. Georgy, et al. Strength sensor for dynamic wheel load measuring of railway carriages.26th International Spring Seminar on Electronics Technology,2003,260-265.
[102]潘立登,吴宁川.径向基函数神经网络正交最小二乘改进算法的实现.北京化工大学学报,2002,29(4)：82-84
[103]邓聚龙.灰色控制系统.华中科技大学出版社,1993
[104]张怡,魏勇,熊常伟.灰色模型GM(1,1)的一种新优化方法.系统工程理论与实践,2007,(4)：141-146
[105]刘思峰.灰色系统理论及其应用(第五版).科学出版社,2010,146-168
[106]C. C. Hsu, C. Y. Chen. Applications of improved grey prediction model for power demand forecasting. Energy Conversion and Management,2003, (44):2241-2249
[107]付继华,孟浩,王中宇.基于灰色理论的动态测量系统非统计建模方法.仪器仪表学报,2008,29(6)：1245-1249
[108]张大海,江世芳,史开泉.灰色预测公式的理论缺陷及改进.系统工程理论与实践,2002,22(8)：140-142
[109]吉培荣,黄巍松,胡翔勇.灰色预测模型特性的研究.系统工程理论与实践,2001,21(9)：105-109
[110]林晓言,陈有孝.基于灰色-马尔科大链改进方法的铁路货运量预测研究.铁道学报,2005,27(3)：15-19
[111]C. H. Wang. Predicting tourism demand using fuzzy times series and hybrid grey theory. Tourism Management,2004,25(3):367-374
[112]周铭,王洪发.基于遗传算法的灰色GM(1,1)模型.南昌大学学报(理科版),2002,12(4)：331-333
[113]L. C. Hsu. Forecasting the output of integrated circuit industry using genetic algorithm based multivariable grey optimization models. Expert System with Applications,2009, 36(4):7898-7903
[114]袁景凌,李小燕,钟珞.遗传优化的灰色神经网络模型比较研究.计算机工程与应用,2010,46(2)：41-43
[115]陈淑燕,王炜.交通量的灰色神经网络预测方法.东南大学学报(自然科学版),2004,34(4)：541-544
[116]T. Y. Pai, Y. P. Tsai, H. M. Lo, et al. Grey and network prediction of suspended solids and chemical oxygen demand in hospital wastewater treatment plant effluent. Computer and Chemical Engineering,2007,31(10):1272-1281.
[117]曹建华,刘渊,代悦.一种基于灰色神经网络的网络流量预测模型.计算机工程与应用,2008,44(5)：155-157.
[118]万星,周建中.改进灰色神经网络模型在电量预测中的应用.水力发电,2007,33(6)：69-72
[119]党耀国,刘思峰,刘斌.以x(1)(N)为初始条件的GM模型.中国管理科学,2005,13(1)：132-135.
[120]任愈,陈建政,林建辉.测力轮对轮轨力检测盲信号分离方案研究.机械科学与技术,2010,29(3)：289-292
[121]C. Jutten, J. Herault. Blind separation of sources, part I:an adaptive algorithm based on neuromimetic architecture. Signal Processing,1991,28:1-10
[122]史习智.盲信号处理-理论与实践.上海交通大学出版社,2008
123] Y. Li, A. Cichocki, S. I. Amari. Sparse component analysis for blind source separation with less sensors than sources. Proceedings of ICA,2003
124] A. Hyvarinen. Fast and robust fixed-point algorithm for independent component analysis. IEEE Trans. On Neural Network,1999,10(3):626-634
125] A. Hyvarinen. E. Oja. Independent component analysis by general nonlinear Hebbian like learning rules. Signal Processing,1998,64(3):301-313
126]姚志湘,粟晖,刘焕彬.针对FastICA计算终点判断的算法改进.计算机工程与应用,2006,42(26)：46-49
127] Z. X. Yao, K. Zhang, H. B. Liu, et al. Eliminate indeterminacies of independent component analysis for chemometrics. Progress in Natural Science,2008, 18(8):1009-1014
128]汪斌,王年,蒋云志,等.改进FastICA算法在谐波检测中的应用.电力自动化设备,2011,31(3)：135-138
129]姚志湘,刘焕彬,粟晖.盲信号分离输出与源信号的一致性判断.华南理工大学学报(自然科学版),2007,35(5)：50-53
130]何昭水,谢胜利,傅予力.稀疏表示与病态混叠盲分离.中国科学E辑：信息科学,2006,36(8)：864-879
131] A. Johansson. Out-of-round railway wheels-assessment of wheel tread irregularities in train traffic. Journal of Sound and Vibration,2006, (293):795-806
132] T. Mazilu. A dynamic model for the impact between the wheel flat and rail. Mechanical Engineering,2007,69(2):45-58
133] M. M. Steenbergen. Wheel-rail interaction at short-wave irregularities. Doctoral dissertation of technology university of Delft,2008
134] D. Lyon. The calculation of track forces due to dipped rail joints, wheel flats and rail weld. The second ORE colloquium on technical computer programs,1972
135]翟婉明.铁路车轮扁疤的动力学效应.铁道车辆,1994,(7)：1-5
136] Wheel impact detection systems-the North American experience. Zeta-Tech Associates, Suite Cherry Hill,1997
137] J. M. Samuels, J. J. Palesano. Using mainline wheel impact detectors to increase the productivity of car inspection functions. Mechanical Engineering Transaction, Institution of Engineers,1988, (3):165-168
138] A. Tajaddini, S. F. Kalay. Time to revise wheel-removal ruels. Railway Age,1995, 196(9):93-98
139] AAR. Association of American railroad rule 41-section A,2002
140]蒋宗礼.人工神经网络导论.高等教育出版社,2001
141] T. Kohonen. Self-organization and associative memory. Springer-Verlag,1987
142]邓乃扬,田英杰.支持向量机-理论、算法与拓展.科学出版社,2009
143] U. Thissen, R. Brakel, A. P. Weijer, et al. Using support vector machines for time series prediction. Chemometrics and Intelligent Laboratory Systems,2003,69(1-2):35-49
[144]L. J. Herrera, H. Pomares, I. Rojas, et al. Recursive prediction for long term time series forecasting using advanced models. Neurocomputing,2007,70(16-18):2870-2880
[145]陈果.基于遗传算法的支持向量机时间序列预测模型优化.仪器仪表学报,2006,27(9)：1080-1084
[146]陈果.基于遗传算法的支持向量机分类器模型参数优化.机械科学与技术,2007,26(3)：347-350
[147]E. Pourbasheer, S. Riahi, M. R. Ganjali, et al. Application of genetic algorithm support vector machine (GA-SVM) for prediction of BK-channels activity. European Journal of Medicinal Chemistry,2009,44(12):5023-5028
[148]B. Samanta, K. R. Al-Balushi, S. A. Al-Araimi. Artificial neural networks and support vector machine with genetic algorithm for bearing fault detection. Engineering Application of Artificial Intelligence,2003,16(7-8):657-665
[149]R. Karoumi, J. Wiberg, A. Liljencrantz. Monitoring traffic loads and dynamic effects using an instrumented railway bridge. Engineering Structures,2005,27(12):1813-1819
[150]N. E. Huang, Z. Shen, S. R. Long. The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis. Proceedings of the Royal Society of London,1998,454(1971):903-995
[151]ZHAO Jinping, HUANG Daji. Mirror extending and circular spline function for empirical mode decomposition method. Journal of Zhejiang University (Science),2001, 2(3):247-252
[152]DENG Yongjun, WANG Wei, QIAN Chengchun, et al. Boundary-processing-technique in EMD method and Hilbert transform. Chinese Science Bulletin,2001,46(11):954-961
[153]刘慧婷,张旻,程家兴.基于多项式拟合算法的EMD端点问题的处理.计算机工程与应用,2004,40(16)：84-86,100
[154]张郁山,梁建文,胡聿贤.应用自回归模型处理EMD方法中的边界问题.自然科学进展,2003,13(10)：1054-1059
[155]CHENG Junsheng, YU Dejie, YANG Yu. Application of support vector regression machines to the processing of end effects of Hilbert-Huang transform. Mechanical Systems and Signal Processing,2007,21(3):1197-1211
[156]邵晨曦,王剑,范金锋,等.一种自适应的EMD端点延拓方法.电子学报,2007,35(10)：1944-1948
[157]陈双喜,林建辉,陈建政.基于改进的EMD方法提取车辆-轨道垂向耦合系统动态特征.振动与冲击,2011,30(8)：212-216
[158]何正嘉.现代信号处理及工程应用.西安交通大学出版社,2007
[159]S. G. Mallat. A wavelet tour of signal processing. Academic Press,1999
[160]杨国安,钟秉林,黄仁,等.机械故障信号小波包分解的时域特征提取方法研究.振动与冲击,2001,20(2)：25-28,31
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