系统可靠性评估的超椭球贝叶斯网络及其灵敏度方法

中国机械工程 ›› 2015, Vol. 26 ›› Issue (4): 529-535,552.

系统可靠性评估的超椭球贝叶斯网络及其灵敏度方法

陈东宁1,2;姚成玉3

1.燕山大学河北省重型机械流体动力传输与控制重点实验室,秦皇岛,066004
2.先进锻压成形技术与科学教育部重点实验室(燕山大学),秦皇岛,066004
3.燕山大学河北省工业计算机控制工程重点实验室,秦皇岛,066004

出版日期:2015-02-25 发布日期:2015-02-25
基金资助:
国家自然科学基金资助项目(51405426);河北省自然科学基金资助项目（E2012203015）；河北省教育厅资助科研项目(ZH2012062）

System Reliability Assessment Method Based on Hyper-ellipsoid Bayesian Networks and Their Sensitivities

Chen Dongning1,2;Yao Chengyu3

1.Hebei Provincial Key Laboratory of Heavy Machinery Fluid Power Transmission and Control, Yanshan University, Qinhuangdao, Hebei,066004
2.Key Laboratory of Advanced Forging & Stamping Technology and Science(Yanshan University),Ministry of Education of China, Qinhuangdao,Hebei,066004
3.Key Laboratory of Industrial Computer Control Engineering of Hebei Province, Yanshan University, Qinhuangdao, Hebei, 066004

Online:2015-02-25 Published:2015-02-25
Supported by:
National Natural Science Foundation of China（No. 51405426）;Hebei Provincial Natural Science Foundation of China（No. E2012203015）；Hebei Provincial Scientific Research Project of Ministry of Education of China（No. ZH2012062）

摘要/Abstract

摘要：

利用区间模型描述根节点的失效可能性,解决根节点的失效可能性不易精确获取的问题；通过引入超椭球模型来界定不确定性参量的取值范围，解决区间贝叶斯网络在求取可靠性指标时计算结果相对保守的问题；定义超椭球贝叶斯网络的灵敏度指标，为找到系统的关键环节提供依据；结合贝叶斯网络双向推理求解出在根节点失效可能性已知的条件下，叶节点的失效可能性、根节点状态的后验可能性;给出了可靠性评估实例。

关键词: 可靠性评估, 贝叶斯网络, 超椭球, 区间模型, 灵敏度

Abstract:

Failure probabilities of root nodes were described by interval model to solve the difficulty to obtain the failure probabilities accurately. Hyper-ellipsoid model was utilized to define the ranges of the uncertain parameters to improve the relatively conservative reliability indices calculated by the interval Bayesian networks method. Sensitivity indices of hyper-ellipsoid Bayesian networks were proposed to provide basis for finding the key link of the system. The leaf node's failure probability and the root nodes' state posterior possibilities were solved by combining with Bayesian networks bidirectional inference under the condition of the root nodes' failure probabilities as known. Reliability assessment example was given at last.

Key words: reliability assessment, Bayesian network, hyper-ellipsoid, interval model, sensitivity

中图分类号:

TB114.3

陈东宁, 姚成玉. 系统可靠性评估的超椭球贝叶斯网络及其灵敏度方法[J]. 中国机械工程, 2015, 26(4): 529-535,552.

Chen Dongning, Yao Chengyu. System Reliability Assessment Method Based on Hyper-ellipsoid Bayesian Networks and Their Sensitivities[J]. China Mechanical Engineering, 2015, 26(4): 529-535,552.

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