论文标题
贝叶斯网络建模和复杂多层系统的可靠性推断的算法：第二部分依赖性系统
Algorithms for Bayesian network modeling and reliability inference of complex multistate systems: Part II-Dependent systems
论文作者
论文摘要
如第一部分所述，使用贝叶斯网络（BN）来构建复杂的多层系统的可靠性模型，节点概率表（NPT）的内存存储要求将超过计算机的随机访问存储器（RAM）。但是，第一部分的提出的推理算法不适用于依赖系统。第二部分提出了一种新颖的方法，用于BN可靠性建模和分析，将压缩想法应用于复杂的多层依赖性系统。在本第二部分中，依赖性节点及其父节点等效于一个块，基于多层关节概率推断算法，提出了计算块所有节点的关节概率分布。然后，基于第I部分的提议的多层压缩算法，为复杂的多层依赖性系统提出了依赖的多态推理算法。在情况1中证明了所提出的算法的使用和准确性。最后，提出的算法应用于卫星态度控制系统的可靠性建模和分析。结果表明，第一部分和第二部分的提议算法使得对复杂的多层系统可行的可靠性建模和分析。
In using the Bayesian network (BN) to construct the complex multistate system's reliability model as described in Part I, the memory storage requirements of the node probability table (NPT) will exceed the random access memory (RAM) of the computer. However, the proposed inference algorithm of Part I is not suitable for the dependent system. This Part II proposes a novel method for BN reliability modeling and analysis to apply the compression idea to the complex multistate dependent system. In this Part II, the dependent nodes and their parent nodes are equivalent to a block, based on which the multistate joint probability inference algorithm is proposed to calculate the joint probability distribution of a block's all nodes. Then, based on the proposed multistate compression algorithm of Part I, the dependent multistate inference algorithm is proposed for the complex multistate dependent system. The use and accuracy of the proposed algorithms are demonstrated in case 1. Finally, the proposed algorithms are applied to the reliability modeling and analysis of the satellite attitude control system. The results show that both Part I and Part II's proposed algorithms make the reliability modeling and analysis of the complex multistate system feasible.
