基于动态贝叶斯网络的列控中心可靠性及可用性评估

交通运输系统工程与信息 ›› 2018, Vol. 18 ›› Issue (3): 182-188.

基于动态贝叶斯网络的列控中心可靠性及可用性评估

江磊¹，王小敏^*1，蔺伟²

1. 西南交通大学交通信息工程及控制重点实验室，成都 610031； 2. 中国铁道科学研究院通信信号研究所，北京 100081

收稿日期:2017-12-11 修回日期:2018-03-27 出版日期:2018-06-25 发布日期:2018-06-25
作者简介:江磊(1986-)，男，四川成都人，博士生.
基金资助:
四川省科技计划重点研发项目/ Key Research and Development Project for Science and Technology of Sichuan Province (2018GZ0195)；国家铁路智能运输系统工程技术研究中心开放课题/ Open Project Fund for the Center of National Railway Intelligent Transportation System Engineering and Technology (RITS2018KF02)；四川省应用基础研究/ Sichuan Province Application Foundation Research Project (2015JY0182).

Reliability and Availability Evaluation of Train Control Center Based on Dynamic Bayesian Network

JIANG Lei¹, WANG Xiao-min¹, LIN Wei²

1. Key Laboratory of Traffic Information Engineering and Control, Southwest Jiaotong University, Chengdu 610031, China; 2. Signal and Communication Research Institute, China Academy of Railway Sciences, Beijing 100081, China

Received:2017-12-11 Revised:2018-03-27 Online:2018-06-25 Published:2018-06-25

摘要/Abstract

摘要：

为解决共因失效、动态失效及恢复机制等问题，本文基于动态贝叶斯网络对联锁车站和中继站列控中心可靠性及可用性进行评估.在分析列控中心系统结构的基础上构建系统动态故障树，并将动态故障树转化为动态贝叶斯网络，实现结构学习和参数学习.通过动态贝叶斯网络正向推理得到两种类型列控中心可靠度和可用度并进行比较分析.通过动态贝叶斯网络反向推理得到列控中心系统薄弱环节.研究系统敏感性因素并讨论恢复机制对系统可靠性及可用性的影响.结果表明：考虑共因失效的联锁车站和中继站列控中心的稳态可用度分别为0.999 960和0.999 977；电源及驱动采集单元为系统薄弱环节，需要重点关注.该方法能有效提高列控中心智能维修维护水平.

关键词: 系统工程, 可靠性评估, 可用性评估, 动态贝叶斯网络, 列控中心, 共因失效

Abstract:

This paper presents a systemic approach to evaluate the reliability and availability of train control center in interlocking station (ITCC) and relay station (RTCC) based on dynamic Bayesian network, aiming to solve the problems such as common cause failure, dynamic failure behavior, and recovery mechanism. Taking account of the systemic architecture, the dynamic fault tree is constructed. By mapping dynamic fault tree to dynamic Bayesian network(DBN), the structure and parameter modeling are conducted. Based on the forward inference of DBN, the reliability and availability of ITCC and RTCC are obtained and compared. Based on the backward inference of DBN, the vulnerabilities of train control center are recognized. Moreover, the sensitivity analysis and the effect of recovery mechanism on reliability and availability are researched. The results show that the availabilities of ITCC and RTCC with common cause failure are 0.999 960 and 0.999 977, respectively; the DY and PIO are the vulnerabilities, which should be paid more attention. The proposed approach can improve the intelligent maintenance level of train control center.

Key words: system engineering, reliability evaluation, availability evaluation, dynamic Bayesian network, train control center, common cause failure

中图分类号:

U283.2

江磊，王小敏，蔺伟. 基于动态贝叶斯网络的列控中心可靠性及可用性评估[J]. 交通运输系统工程与信息, 2018, 18(3): 182-188.

JIANG Lei, WANG Xiao-min, LIN Wei. Reliability and Availability Evaluation of Train Control Center Based on Dynamic Bayesian Network[J]. Journal of Transportation Systems Engineering and Information Technology, 2018, 18(3): 182-188.

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链接本文: http://www.tseit.org.cn/CN/abstract/abstract19644.shtml

http://www.tseit.org.cn/CN/Y2018/V18/I3/182

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