摘要
针对利用β因子模型对列控车载子系统共因失效问题进行可靠性量化分析时存在计算结果不精确的问题,采用α因子模型对共因失效进行计算,并综合考虑恢复机制和降级运行因素,对列控车载子系统进行可靠性分析。在分析列控车载子系统功能结构的基础上,通过故障树和贝叶斯网络之间的映射关系,建立车载子系统的贝叶斯网络模型;利用β因子模型和α因子模型分别对车载子系统共因失效问题进行量化分析,再以新增共因失效节点的方式建立考虑共因失效的车载子系统贝叶斯网络。结果表明:贝叶斯网络双向推理能力能用于分析车载子系统的可用度和薄弱环节,应尽量减小故障降级运行对系统可用度的影响;通过对列控车载子系统共因失效故障数据的不断累积,α因子模型的量化分析结果更贴合实际的共因失效率。
To deal with the inaccurate calculation results during the use of the Beta factor model to quantitatively analyze the common cause failure(CCF)of the onboard subsystem,this paper presented the Alpha factor model to analyze the CCF,and comprehensively considered recovery mechanism and degrading operation factors to analyze the reliability of onboard subsystem.Based on the analysis of the systemic architecture and function of the onboard subsystem,the Bayesian network model of the onboard subsystem was established through the mapping relationship between the fault tree and Bayesian network.The Beta and Alpha factor models were used to quantitatively analyze the CCF of the onboard subsystem,and the Bayesian network of the CCF failures was established by adding CCF nodes.The results show that the bidirectional reasoning ability of Bayesian network can be used to analyze the availability and weakness of the onboard subsystem,to minimize the influence of fault degradation operation on system availability.By accumulating CCF data of on-board subsystem,the quantitative analysis results of Alpha factor model are more suitable for the actual CCF rate.
作者
张振海
王悦榕
党建武
ZHANG Zhenhai;WANG Yuerong;DANG Jianwu(School of Automation and Electrical Engineering,Lanzhou Jiaotong University,Lanzhou 730070,China;Gansu Engineering Research Center of Artificial Intelligence and Graphics and Image Processing,Lanzhou 730070,China)
出处
《铁道学报》
EI
CAS
CSCD
北大核心
2021年第9期63-70,共8页
Journal of the China Railway Society
基金
国家自然科学基金(61763025)。
关键词
可靠性分析
α因子模型
贝叶斯网络
车载子系统
reliability analysis
Alpha factor parameter model
Bayesian networks
onboard subsystem
