Reliability analysis of multi-state systems with common cause failures based on Bayesian network and fuzzy probability
Reliability analysis of multi-state systems with common cause failures based on Bayesian network and fuzzy probability
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DOI：
10.1007/s10479-019-03247-6
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发表时间：
2022-04-01
影响因子：
4.8
通讯作者：
Han, Xiaomeng
中科院分区：
文献类型：
--
作者：
Li, Yan-Feng;Huang, Hong-Zhong;Han, Xiaomeng
多状态部件、共因故障和数据不确定性是复杂工程系统可靠性分析中普遍存在的问题。提出了一种将模糊概率和贝叶斯网络(BN)结合到具有CCF的多态系统(MSSS)中的方法。特别是发展了多状态贝叶斯网络和模糊概率的基本理论。此外，还给出了CCF与BN的集成模型。为了将模糊概率引入到考虑CCF产生的公共父节点的MSSS可靠性评估中，必须将模糊概率通过反模糊化和归一化方法转化为精确概率，这两种方法都是详细阐述的。此外，还进行了基于BN的定量分析。文中以数控机床主轴进给系统为例，验证了该方法的可行性。提高了神经网络对具有不确定性问题的复杂系统进行可靠性评估的能力。
Multi-state components, common cause failures (CCFs) and data uncertainty are the general problems for reliability analysis of complex engineering systems. In this paper, a method incorporating fuzzy probability and Bayesian network (BN) into multi-state systems (MSSs) with CCFs is proposed. In particular, basic theories of multi-state BN and fuzzy probability are developed. Moreover, a model integrating CCFs with BN has also been illustrated. In order to incorporate fuzzy probability into MSSs reliability evaluation considering common parent node generated by CCFs, fuzzy probability has to be translated into accurate probability through defuzzification and normalization methods which are both elaborated. In addition, quantitative analysis based on BN is carried out. In this paper, feed system of boring spindle in computer numerical control machine is analyzed as an example to validate the feasibility of the proposed method. It can improve the ability of BN on reliability evaluation of complex system with uncertainty issues.