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一种考虑共因失效的PMS可靠性建模分析方法 被引量:4

A reliability modeling and analysis method for PMS considering common cause failure
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摘要 共因失效(CCF)打破了系统内组件失效的独立性假设,会对系统特别是多阶段任务系统(PMS)的可靠性评估产生显著影响。针对多阶段任务系统中随机共因失效(PCCF)对任务可靠性的影响问题,对共因事件之间的关系进行分析,利用贝叶斯理论扩展了共因事件的概率模型,使其适用于互斥、相互独立和统计相关等多种统计关系。在此基础上提出了综合应用二元决策图(BDD)和马尔可夫(Markov)模型的模块化建模分析方法。首先,利用故障树对任务过程建模;然后,在考虑共因失效的情况下采用BDD和Markov模型分别计算系统中静态模块和动态模块;再次,由全概率公式计算任务可靠性;最后,以卫星首次转轨过程为对象,验证了方法的有效性,并通过与已有案例的对比,分析了共因失效对任务可靠性的影响。 Common cause failures(CCFs) in a system destroy the hypothesis that the failures are independent,which may significantly impact the reliability evaluation of the system,especially the phased-mission system(PMS). Aimed at the impact of probabilistic common cause failure(PCCF) on reliability of mission in PMS,this paper discussed the relationship between common cause events and extended the probabilistic model of common cause events using Bayesian theory to make the model fit for different statistical relations including mutually exclusive,s-independent and s-dependent. Moreover,a module-based modeling and analysis method using binary decision diagram(BDD) and Markov model was proposed. First,the fault tree of each phase was constructed. Then,considering CCF,BDD and Markov model were used to deal with the static and dynamic module in PMS respectively. Third,mission reliability was evaluated using total probability law. Finally,a case study of satellite for its orbit transfer was supplied to verify the effectiveness of the method. In addition,the result of this paper was compared with the existing case to analyze the influence of CCFs on mission reliability.
作者 吴欢 焦健 赵廷弟 WU Huan;JIAO Jian;ZHAO Tingdi(School of Reliability and Systems Engineering, Beijing University of Aeronautics and Astronautics, Beijing 100083, China)
出处 《北京航空航天大学学报》 EI CAS CSCD 北大核心 2018年第5期1088-1094,共7页 Journal of Beijing University of Aeronautics and Astronautics
关键词 多阶段任务系统(PMS) 随机共因失效(PCCF) 二元决策图(BDD) MARKOV模型 动态性 phased-mission system (PMS) probabilistic common cause failure (PCCF) binary decision diagram (BDD) Markov model dynamic
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