融合不确定信息的某型导弹控制系统可靠性分析方法  

Reliability analysis of a missile control system by fusing uncertain information

作　　者：李志强[1] 徐廷学[1] 顾钧元[1] 安进[1] 刘玉东[2] LI Zhiqiang;XU Tingxue;GU Junyuan;AN Jin;LIU Yudong(Deparimeni of Ordnance Science and Technology,Naval Aeronautical ＆ Asironauiical University,Yantai 264001, China;Unit 95080 of the PLA,Shantou 515000, China)

机构地区：[1]海军航空工程学院兵器科学与技术系 [2]中国人民解放军95080部队

出　　处：《系统工程与电子技术》2017年第12期2869-2876,共8页Systems Engineering and Electronics

基　　金：国家自然科学基金(51605487);中国博士后科学基金(2016M592965);山东省自然科学基金(ZR2016FQ03)资助课题

摘　　要：针对不确定信息在可靠性评估中难以表达与处理的问题,应用Dempster-Shafer(DS)证据理论对贝叶斯网络进行改进。在分析现有研究的基础上,对DS证据理论与贝叶斯网络理论进行简要介绍,提出了不确定信息条件下故障树节点向贝叶斯网络节点转化的方法,包括与节点、或节点、异或节点、非节点与2/3表决节点。针对多状态贝叶斯网络中条件概率值难以确定的问题,应用DS证据理论/层次分析法对专家经验知识进行分析与表达。以某型导弹控制系统为例,利用故障树构建贝叶斯网络模型,应用DS证据理论对专家信息进行数据融合处理,确定不确定节点的信任函数、似然函数和条件概率值,并借助贝叶斯网络的正向推理、反向推理和重要度分析确定了可靠性设计与分配中的薄弱节点。Aiming at the problem in expressing and dealing with uncertain information in reliability evaluation,Dempster-Shafer（DS）evidence theory is applied to modify Bayesian network.On the basis of the existing research,DS evidence theory and Bayesian network are introduced briefly,and conversion methods that convert fault trees to Bayesian network under uncertain conditions are proposed,including AND node,OR node,XOR node,NOT node and 2/3 VOTE node.Aiming at the difficulty in determining conditional probability values of multi-state Bayesian network,a method based on DS evidence theory and analytic hierarchy process is proposed to analyze experts’ experience.In the end,a missile control system is taken for example.Bayesian network model is established by referring to the fault tree,and DS evidence theory is used to determine the belief functions and plausibility functions of uncertain nodes and conditional probability values by fusing experts’ information.Weak node in reliability design and distribution is pointed out by forward reasoning,backward reasoning and importance analysis of Bayesian network.

关 键 词：不确定信息 Dempster-Shafer(DS)证据理论 贝叶斯网络 故障树 重要度分析 

分 类 号：TB114.3[理学—概率论与数理统计]