基于故障数据的计算机联锁系统寿命预测方法

陈建译; 周荣; 乔高锋; 王海峰; 徐田华

基于故障数据的计算机联锁系统寿命预测方法

1.
广州铁路(集团)公司电务处, 广州 510000;
2.
北京全路通信信号研究设计院集团有限公司,北京 100070;
3.
北京交通大学轨道交通运行控制系统国家工程研究中心,北京 100044;
4.
北京交通大学轨道交通控制与安全国家重点实验室,北京 100044

基金项目: 中国铁路总公司科技研究开发计划课题（2015X007-G）；国家自然科学基金（61473029）。

详细信息

作者简介:
陈建译，教授级高级工程师；周荣，高级工程师。

中图分类号: U284.3:TP39
出版历程
- 收稿日期: 2016-10-28
- 刊出日期: 2017-01-25

Life prediction for railway computerized interlocking system based on fault data

1.
Department of Signal & Communication, Guangzhou Railway (Group) Corporation, Guangzhou 510000, China;
2.
Beijing National Railway Research & Design Institute of Signal & Communication Co. Ltd., Beijing 100070, China;
3.
National Engineering Research Center of Rail Transportation Operation and Control System, Beijing Jiaotong University, Beijing 100044,China;
4.
State Key Laboratory of Rail Traffic Control and Safety, Beijing Jiaotong University, Beijing 100044, China

摘要

摘要: 文章从计算机联锁系统原理和硬件结构出发，提出了基于现场运维故障数据的系统寿命预测方法。利用故障树分析方法对计算机联锁系统的体系结构进行分析，求解最小割集，找到影响系统使用寿命的关键因素；将板级故障数据按照指数分布、威布尔分布的统计分布规律进行归类，使寿命预测模型更加贴近实际。最后，以我国铁路具有代表性的A/B型计算机联锁系统为例，验证了该方法的可行性。提出的计算机联锁系统寿命预测方法可为类似信号设备的运维管理提供借鉴。
- 计算机联锁 /
- 寿命预测 /
- 故障数据 /
- 运维管理
Abstract: Based on the analysis of principle and hardware architecture of computerized interlocking system, this paper proposed a novel method of life prediction by using fault data. The fault tree model of the system was built, from the calculation of the minimum cut set of the fault tree, the key factors that affecting the service life were achieved; and the board-level fault data was classified in accordance with the Exponential and Weibull distribution statistical rules, which made the life prediction model more practical. Finally, taken a widely applied A/B computerized interlocking system as an example, the simulation result showed that the proposed method was feasible. The work in this paper could provide reference for the maintenance management of similar signaling equipment.
- computerized interlocking system /
- life prediction /
- fault data /
- maintenance management