An iterative information integration method for multi-level system reliability analysis based on Bayesian Melding Method

Abstract

For uncertainty modeling and reliability analysis of a complex system, there generally exists multi-source information, which should be fully considered and used synthetically. Research shows that the Bayesian Melding Method (BMM) is a useful tool to merge the multi-source information. However, how to apply BMM for the complex system with a multi-level hierarchical structure remains a challenging issue. To address this problem, this paper proposes an iterative information integration method for multi-level system structures so as to fully integrate the information between different levels. A complete single iteration consists of the updating process from the system bottom to top level and then from the system top to bottom level. To facilitate the updating process, the complex multi-level system is first decomposed into several basic double-level units, within which the information integration can be conveniently conducted with the proposed discrete or continuous BMM methods. To check the iteration convergence, the symmetric Kullback-Leibler Divergence (SKLD) is adopted to measure the difference between the updated system distributions obtained in the two consecutive iteration processes.Finally, three case studies with discrete and continuous information integration problems are used to demonstrate and validate the proposed method.