自主定位信息辅助的列车完整性检测风险分析

李四辉; 蔡伯根; 徐爱国; 上官伟; 闻映红; 王剑

doi:10.3969/j.issn.0258-2724.2017.05.007

自主定位信息辅助的列车完整性检测风险分析

doi: 10.3969/j.issn.0258-2724.2017.05.007

李四辉¹,
蔡伯根^1,2,
徐爱国³,
上官伟^1, ,,
闻映红^1,2,
王剑^1,2

基金项目:

国家国际科技合作专项资助项目（2014DFA80260）

国家自然科学基金资助项目（U1334211，U1234205，61403021）

中央高校基本科研业务费专项资金资助项目（2015YJS021）

详细信息

作者简介:
李四辉(1989-),男,博士研究生,研究方向为列车完整性检测,E-mail:12111053@bjtu.edu.cn

通讯作者:
上官伟(1979-),男,教授,博士,研究方向为智能交通控制,E-mail:wshg@bjtu.edu.cn

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出版历程
- 收稿日期: 2016-05-30
- 刊出日期: 2017-10-25

Autonomous-Positioning Information Aided Train Integrity Detection Risk Analysis Method

摘要

摘要: 针对自主定位信息错误或失效导致的列车完整性检测安全风险问题，在列车完整性检测问题进行分析的基础上，基于贝叶斯风险综合列车列首及列尾相对位置、相对速度及相对加速度信息，建立了列车完整性检测风险分析模型.该模型结合各项自主定位信息的检测失效概率及检测概率，分别计算了列车完整性检测虚警风险及失效风险.在此基础上，结合现场测试数据及仿真数据针对列车完整性检测风险分析方法进行了测试验证，结果表明：在列车完整场景中虚警风险结果为10-1.8，大于检测失效风险10-5.5；在列车非完整场景中，巡航和最大牵引条件下失效风险分别为10-1.8和1，分别大于虚警风险10-4.5和0，结果符合列车完整性真实状态下的检测风险.
- 列车完整性检测 /
- 自主定位信息 /
- 极限状态 /
- 贝叶斯风险
Abstract: Autonomous-positioning information error and failure can lead to a safety risk in train integrity detection. In this study, the problem of train integrity detection, which determines whether a train remains intact in terms of relative position, velocity and acceleration, was investigated. A Bayesian risk-based train integrity detection risk analysis model was proposed to determine both the false-alarm rate and failure rate of autonomous-positioning information based train integrity detection. In this model, both the misdetection probability and failure probability of each autonomous-positioning information were applied to find the misdetection rate and false-alarm rate of train integrity detection. Then, a simulation based on field data was conducted to verify the train integrity detection risk analysis method. The results show that the risk of false alarms is 10-1.8, which is larger than the risk of misdetection (10-5.5) in the complete train scenario. In contrast, in the train break scenario, the risk of misdetection is 10-1.8 when cruising and 1 when accelerating, which are larger than the risk of false alarms (10-4.5 when cruising and 0 when accelerating). The results conform to the train integrity states, indicating that the risk analysis model has sufficient capability to calculate the risk of autonomous-positioning information aided train integrity detection.
- train integrity detection /
- autonomous-positioning information /
- limit state /
- bayesian risk

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参考文献(14)

温丽健,陆承超,南丁. 移动闭塞条件下列车完整性检查的方案探讨[J]. 中国科技信息,2010(2):262-263. WEN Li Jian, LU Cheng Chao, NAN Ding. Scheme discussion of Train integrity detection under conditions of moving block[J]. China Science and Technology, 2010(2):262-263.

李伟. GPS辅助列车完整性监测系统的设计与实现[D]. 北京:北京交通大学,2012.

安毅,蔡伯根,宁滨,等. 基于GPS和虚拟卫星的列车完整性检查方法研究[J]. 铁道学报,2012,34(9):40-44. AN Yi, CAI Baigen, NING Bin, et al. Research on train integrity monitoring method based on GPS and virtual-satellite[J]. Journal of the China railway society, 2012, 34(9):40-44.

陈晓乾. GPS在列车完整性检查中的应用研究[D]. 北京:北京交通大学,2006.

王剑,靳成铭,蔡伯根,等. 面向我国低密度线路的列车运行控制系统[J]. 铁道学报,2015,37(12):46-52. WANG Jian, JIN Chengming, CAI Baigen, et al. A train control system for low density line in China[J]. Journal of the China railway society, 2015, 37(12):46-52.

ACHARYA A, SADHU S, GHOSHAL T K. Train localization and parting detection using data fusion[J]. Transportation Research Part C Emerging Technologies, 2011, 19(1):75-84.

JANA H. GNSS train position integrity monitoring by the help of discrete PIM algorithms[J]. Journal of Applied Mathematics, 2009, 2(2):73-80.

SCHOEMAKER M. Train-spotting,a WSN-Based train integrity system[C]//Proc. of Eighth International Conference on Networks.Washington D. C.:IEEE Press, 2009:226-231.

SCHOLTEN H, WESTENBERG R, SCHOEMAKER M. Sensing train integrity[C]//Proc. of SENSORS.Canterbury:IEEE Press, 2009:669-674.

杨克须. 基于多传感器的列车完整性检测技术研究[D]. 北京:北京交通大学,2014.

刘江. 基于鲁棒估计理论的列车组合定位方法研究[D]. 北京:北京交通大学,2011.

MING C, Dong W, JI Y. A distributed approach for track occupancy detection[J]. Control Engineering Practice, 2014, 22(1):69-80.

PAVLOVIC V, REHG J, CHAM T. A dynamic bayesian network approach to figure tracking using learned dynamic models[C]//Proc of Conf. Computer Vision.Corfu:IEEE Press, 1999:94-101.

PAVLOVIC V, REHG J M, CHAM T J. A dynamic bayesian network approach to tracking using learned switching dynamic models[C]//Proc. of Conf. Hybrid Systems:Computation and Control.Pittsburgh:IEEE Press, 2000:366-380.

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