Open AccessAn information aggregation method for hierarchical system reliability analysis with insufficient reliability information based on Bayesian Melding Method
Author(s) -
Yingchun Xu,
Wen Yao,
Xiaoqian Chen,
Jianfeng Liu,
Sheng Wang,
Xing Chen,
Tao Zhou
Publication year - 2021
Publication title -
iop conference series. materials science and engineering
Language(s) - English
Resource type - Journals
eISSN - 1757-899X
pISSN - 1757-8981
DOI - 10.1088/1757-899x/1043/2/022007
Subject(s) - computer science , reliability (semiconductor) , merge (version control) , data mining , bayesian probability , reliability engineering , inference , bayesian inference , bayesian network , information system , machine learning , artificial intelligence , information retrieval , engineering , power (physics) , physics , quantum mechanics , electrical engineering
In practical engineering, the reliability analysis of the hierarchical system structure is a hot topic, which has made great progress in recent years. From former experiments, there exists multi-source reliability information for the complex system. In the literature, the Bayesian Melding Method is a useful tool to merge the subsystem and system information based on the deterministic structure. However, when the system reliability information is insufficient, how to integrate the limited information for the system reliability analysis remains a problem to be solved. In this paper, an information aggregation method is developed for the hierarchical system to make full use of the limited reliability information based on the traditional Bayesian Melding Method. Three main steps are involved in the proposed method, i.e. distribution assumption, structure reconstruction, and reliability inference, based on which the existed reliability information can be fully merged and partial missing reliability information can be acquired through the system structure. Finally, the benefit illustration and effectiveness verification of the proposed method are given in the numerical example.