A Bayesian Reliability Assessment of Complex Systems for Binomial Sampling

This paper presents a reliability assessment procedure that systematically combines complete system binomial test data with lower level binomial test data obtained from either partial system or component tests. The procedure uses beta prior distributions of all reliabilities, Bayes theorem, and probability moments. The result is a posterior distribution of system reliability that can be used to determine Bayes point and interval estimates. The beta prior distributions evolve from data on predecessor systems similar to the system in question and engineering knowledge about what the various test-alternatives measure.