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列车振动信号与轨道平整度的关系模型仿真 被引量:1

Train Vibration Signals and Rail Flatness of the Relational Model Simulation
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摘要 轨道不平会引起列车的振动,低频振动信号的特征可以很好的描述轨道与列车振动的关系,但是,低频信号特征描述的振动区域有限,传统方法通过建立车辆—轨道垂横耦合模型来识别这种振动信号,无法描述多种不同的车型带来的振动。为解决上述问题,提出基于多渠道差异采集的轨道不平低频传感信号关联模型。对不同车型下的低频信号传输过程中的衰减程度进行估计,计算实际衰减量,并对其进行补偿。对补偿后的传感信号进行分数阶域傅里叶变换,获取信号采集目标函数,完成振动与平整度的关系建模。实验结果表明,利用上述方法进行轨道不平低频传感信号采集,能够获取不同形式的轨道不平整与列车振动的关系。 Orbit uneven will cause the vibration of the train, and the characteristics of low frequency vibration signal can describe the relationship between the track and train vibration well. However, the vibration area described by characteristics of low frequency signal is limited. The traditional methods identified the vibration signal by establishing vehicles - track vertical cross coupling model, but they cannot describe a variety of vibration of different vehicle mod- els. In order to solve above problem, this paper presents a model of orbit uneven low-frequency sensing signal based on multi-channel different collection. The attenuation of the low frequency signals of different models in the process of transmission is estimated, the actual attenuation is calculated and compensated. The sensing signal after compensa- tion is made based ob fractional Fourier transform, the signal acquisition target function is obtain, and the relationship of vibration and flatness is modeled. The experimental results show that this method used to collect orbit uneven low frequency sensor signal can obtain the relationship of the different forms of orbital uneven and the train vibration.
作者 黎远松 雷航
出处 《计算机仿真》 CSCD 北大核心 2014年第9期182-185,共4页 Computer Simulation
基金 四川省教育厅科研项目(13ZAO125) 软件工程专业综合改革项目(ZG-1202) 企业信息化与物联网测控技术四川省高校重点实验室开放基金项目(2014WZY05) 2014WZY03 软件工程专业综合改革:B12201002
关键词 轨道不平整 低频传感信号 多渠道差异采集 Orbital uneven Low frequency sensing signal Differences between multi-channel acquisition
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  • 1刘常昱,李德毅,杜鹢,韩旭.正态云模型的统计分析[J].信息与控制,2005,34(2):236-239. 被引量:210
  • 2李德毅,孟海军,史雪梅.隶属云和隶属云发生器[J].计算机研究与发展,1995,32(6):15-20. 被引量:1261
  • 3高海兵,周驰,高亮.广义粒子群优化模型[J].计算机学报,2005,28(12):1980-1987. 被引量:102
  • 4杨淑莹.模式识别与智能计算[M].北京:电子工业出版社,2008.
  • 5Vapnik V.Statictical learning theory[M].New York:Wiley,1998:11-23.
  • 6Suykens J,Vandewa Lie J.Least squares support vector machine classifiers[J].Neural Processing Letters,1999,9(3):293-300.
  • 7Osuna E,Freuud R,Girosi F.Training support vector machines:An application to face detection[C]//IEEE Computer Society Conference on Computer Vision and Pattern Recognition,1997:130-136.
  • 8Mukerjee S,Osuna E,Girosi F.Nonlinear prediction of chaotic time series using a support vector machine[C]//Principle J,Giles L,Morgnn N.Proceedings of the 1997 IEEE Workshop on Neural Networks for Signal Precessing.[S.l.]:IEEE Press,1997:1125-1132.
  • 9Keerthi S,Shevade S,Bhattcharyya C,et al.A fast iterative nearest point algorithm for support vector machine classifier design[J].IEEE Trans on Neural Networks,2000,11 (1):124-136.
  • 10Frieb T,Chistianini N,Campbell C.The kernel adatron algorithm:A fast and simple learning procedure for support vector machines[C]//Shavlik J.Proceedings of the The 15th International Conference of Machine Learning.CA:Morgan Kanfmann Publishers,1998:188-196.

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