System reliability assessment with multilevel information using the Bayesian melding method

•Investigate methods for integrating multilevel prior information and data through Bayesian melding method.•Update subsystem prior and posterior using system level reliability prior and data.•Posterior reliability inferences using an adaptive sampling importance re-sampling method under multilevel priors and data.•Evaluate system reliability assessment outcomes with varying sample sizes. This paper investigates the Bayesian melding method (BMM) for system reliability analysis by effectively integrating various available sources of expert knowledge and data at both subsystem and system levels. The integration of multiple priors is investigated under both linear and geometric pooling methods. The aggregated system prior distributions using various pooling methods including the BMM are evaluated and compared. Based on these integrated and updated prior distributions and three scenarios of data availability from a system and/or subsystems, methods for posterior system reliability inference are proposed. Computational challenges for posterior inferences using the sophisticated BMM are addressed using the adaptive sampling importance re-sampling (SIR) method. A numerical example with simulation results illustrates the applications of the proposed methods and provides insights for system reliability analysis using multilevel information.