For the important issues of security service of rail vehicles, the online quantitative security assessment method of the service status of rail vehicles and the key equipments is urgently needed, so the method based on safety region was proposed in the paper. At first, the formal description and definition of the safety region were given for railway engineering practice. And for the research objects which their models were known, the safety region estimation method of system stability analysis based on Lyapunov exponent was proposed; and for the research objects which their models were unknown, the data-driven safety region estimation method was presented. The safety region boundary equations of different objects can be obtained by these two different approaches. At last, by real-time analysis of the location relationship and generalized distance between the equipment service status point and safety region boundary, the online safety assessment model of key equipments can be established. This method can provide a theoretical basis for online safety evaluation of trains operation; furthermore, it can provide support for real-time monitoring, early warning and systematic maintenance of rail vehicles based on the idea of active security .a
KeywordsSafety RegionService Status of Rail VehiclesSecurity AssessmentLyapunov ExponentData-Driven
A. E. Mohamed and A. A. Essam, “Framework for Identification of Power System Operating Security Regions,” The Third International Conference on Network and System Security, Gold Coast, 19-21 October 2009, pp. 415419.
Y. X. Yu, “Review of Study on Methodology of Security Regions of Power System,” Journal of Tianjin University, Vol. 41, No. 6, 2008, pp. 635-646 (Ch).
Y. Zhang, Y. Qin, L. M. Jia, et al., “Research on Method Framework of Security Region Estimation in Rail Transit System Operation Safety Assessment,” Journal System Simulation Technology and Its Applications, Vol. 13, 2011, pp. 1018-1022.
Y. Zhang, Y. Qin and L. M. Jia, “Research on Methodology of Security Region Estimation of Railway System Operation Safety Assessment,” Proceedings of World Congress on Engineering and Technology, Vol. 6, 2011, pp. 803-807.
L. Q. Zhang, Y. Shi, T. W. Chen and B. Huang, “A New Method for Stabilization of Networked Control Systems with Random Delays,” Proceedings of the 2005 American Control Conference, Portland, 8-10 June 2005, pp. 633637. http://dx.doi.org/10.1109/ACC.2005.1470028
N. Wang, Y. J. Zhi, J. H. Zhang, et al., “Identity-Based Secure Inter-Domain Routing Protoco1,” Journal of Software, Vol. 20, No. 12, 2009, pp. 3223-3239. http://dx.doi.org/10.3724/SP.J.1001.2009.03396
L. Jin and G. Qian, “Info-Securitv of Expressway Network Collect Fees System,” Computer Engineering, Vol. 29, No. 10, 2006, pp. 124-126.
M. Sabhnani and G. Serpen, “Application of Machine Learning Algorithms to KDD Intrusion Detection Dataset within Misuse Detection Contex,” Proceedings of the International Conferences on Machine Learning, Models, Technologies and Applications, 2003, pp. 209-215.
R. J. Kaye and F. F. Wu, “Dynamic Security Regions of Power Systems,” IEEE Transactions on Circuit and System, Vol. 29, No. 9, 1982, pp. 612-623. http://dx.doi.org/10.1109/TCS.1982.1085203
Y. X. Yu and F. Feng, “Active Power Steady—State Security Region of Power System,” Science in China: Series A, Vol. 33, No. 121, 1990, pp. 1488-1500.
Y. Zeng, J. C. Fan, Y. X. Yu, et al., “Practical Dynamic Security Regions of Bulk Power Systems,” Automation of Electric Power Systems, Vol. 1, 2001, pp. 6-10.
Y. Makarov, P. Du, S. Lu and T. B. Nguyen, “Pacific Northwest National Laboratory,” Wide Area Security Region Intermediate Report, 2009.
X. M. Wu and Z. X. Han, “Steady Security Region Analysis of Power Systems Based on Fuzzy Set Theory,” Proceedings of the EPSA, Vol. 6, No. 3, 1994, pp. 15-20.
A. Essam and A. Mohamed, “Application of Operating Security Regions in Power Systems,” IEEE PES Transmission and Distribution Conference and Exposition, New Orleans, 19-22 April 2010, pp. 1-6.
Y. Zhang, Y. Qin and L. M. Jia, “Security Region Estimation of Peak Amplitudes of Track Irregularities Based on Danger Points Distribution Ratio and SVM,” Journal of Central South University (Science and Technology), Vol. 43, No. 11, 2012, pp. 4533-4541.
X. S. Jin, L. Ling, X. B. Xiao, et al., “Dynamic Behaviour Numerical Simulation and Safety Boundary Analysis for High Speed Trains in Severe Environments,” Computer Aided Engineering, Vol. 20, No. 3, 2011, pp. 29-41, 59.
J.-B. Guo, S.-P. Yang, G.-S. Gao and Q.-P. Zhu, “Stable Current-Collecting and Lyapunov Exponents for HighSpeed Train Pantograph-Catenary’s System,” China Safety Science Journal, Vol. 15, No. 3, 2005, pp. 61-64.
T. X. Wu and M. J. Brennan, “Basic Analytical Study of Pantograph-Catenary System Dynamics,” Vehicle System Dynamics, Vol. 30, No. 6, 1998, pp. 443-456. http://dx.doi.org/10.1080/00423119808969460
G. Poetsch, J. Evans, R. Meisinger, W. Kortüm, W. Baldauf, A. Veitl and J. Wallaschek, “Pantograph/Catenary Dynamics and Control,” Vehicle System Dynamics, Vol. 28, No. 2-3, 1997, pp. 159-195. http://dx.doi.org/10.1080/00423119708969353
R. T. Hu, X. Z. Zhe and L. Y. Ren, “Faults Diagnosis Based on Lyapunov Exponent and Phase Trajectory for a Generator Sets,” Journal of Northeastern University (Natural Science), Vol. 25, No. 5, 2004, pp. 478-481.
L. B. Zhang, C. H. Wang and L. Z. Tian, “Lyapunov Exponent Recognition for the Fault Signal of Mechanical Equipment,” Oil Field Equipment, Vol. 33, No. 2, 2004, p. 527.
W. P. Yang, G. D. Chen and B. Q. Shi, “Application of Lyapunov Exponents to Fault Diagnosis of Diesel Engines,” Journal of Vibration Engineering, Vol. 15, No. 4, 2002, pp. 476-478.
Y. T. Li, “Chaos and Lyapunov Characteristic Exponents,” Physics, Vol. 25, No. 5, 1996, pp. 282-286.
D. Aniszewska and M. Rybaczuk, “Lyapunov Type Stability and Lyapunov Exponent for Exemplary Multiplicative Dynamical Systems,” Nonlinear Dynamics, Vol. 54, No. 4, 2008, pp. 345-354. http://dx.doi.org/10.1007/s11071-008-9333-7
K. Geist, U. Parlitz and W. Lauterborn, “Comparison of Different Methods for Computing Lyapunov Exponents,” Progress of Theoretical Physics, Vol. 83, No. 5, 1990, pp. 875-893. http://dx.doi.org/10.1143/PTP.83.875
A Wolf, J. B. Swift, H. L. Swinney, et al., “Determining Lyapunov Exponents from a Time Series,” Physica D, Vol. 16, No. 3, 1985, pp. 285-317. http://dx.doi.org/10.1016/0167-2789(85)90011-9
K. H. Sun, G. Q. Tan, L. Y. Sheng and T. S. Zhang, “Design and Implementation of Lyapunov Exponents Calculating Algorithm,” Computer Engineering and Applications, Vol. 35, No. 2, 2004, pp. 12-14.
J. H. Ma, Y. S. Chen and Z. R. Liu, “The Matric Algorithm of Lyapunov Exponent for the Experimental Date Obtained in Dynamic Analysis,” Applied Mathematics and Mechanics, Vol. 20, No. 9, 1999, pp. 919-927.
B. G. Tian, “The Calculation of Lyapunov Exponents Based on the Neural Network,” Systems Engineering Theory & Practice, Vol. 8, 2001, pp. 9-13.
H. F. Liu, Y. Y. Zhao and Z. H. Dai, “Calculation of the Largest Lyapunov Exponent in the Discrete Dynamical System with Wavelet Analysis,” Acta Physica Sinica, Vol. 50, No. 12, 2001, pp. 2311-2316.
C. Cortes and V. Vapnik, “Support-Vector Networks,” Machine Learning, Vol. 20, No. 3, 1995, pp. 273-297. http://dx.doi.org/10.1007/BF00994018
T. Pahikkala, S. Pyysalo, J. Boberg, et al., “Improving the Performance of Bayesian and Support Vector Classifiers in Word Sense Disambiguation using Positional Information,” Proceedings of the International and Interdisciplinary Conference on Adaptive Knowledge Representation and Reasoning, Espoo, 2005, pp. 90-97..
C. X. Cui, “Intelligent Classification,” China Meteorological Press, Beijing, 2009.
X. L. Li, A. H. Li and X. F. Bai, “Robust Face Recognition Using HM M and SVM,” Opto-Electronic Engineering, Vol. 37, No. 6, 2010, pp. 103-107.
X. C. Shi and Z. T. Guo, “Mulit-Classification Method of GA-VM on Identifying Grade of Expansive Soils,” Journal of Civil, Architectural & Environmental Engineering, Vol. 31, No. 4, 2009, pp. 44-48, 59.
C. F. Lin and S. D. Wang, “Fuzzy Support Vector Machines with Automatic Membership Setting,” Studies in Fuzziness and Soft Computing, No. 177, 2005, pp. 233254.
R. Zhang, Y. C. Ma and X. B. Duan, “New Algorithm of Fuzzy Support Vector Machine for Classification with Outliers,” Journal of Xi’an Jiaotong University, Vol. 41, No. 12, 2007, pp. 1414-1417.
H. K. Che, F. Z. Lu and Z. Q. Xiang, “Defects Identification by SVM-DS Fusion Decision-Making with Multiple Features,” Journal of Mechanical Engineering, Vol. 46, No. 16, 2010, pp. 101-105. http://dx.doi.org/10.3901/JME.2010.16.101