Liquid Propulsion System Reliability Estimation using Computational Bayesian Approach with Multi-level Data

Abstract

Liquid propulsion system (LPS) is a highly reliable and complex system that is used for the military and space applications. It consists of many flight critical components arranged in series configuration. Reliability is the most critical parameter for this system, even one subsystem failure leads to total failure of flight vehicle. Determining the achieved reliability of a LPS is a unique challenge for designer of these systems. The system reliability needs to be estimated with limited number of tests due to the destructive nature of tests, time and cost constraints. In this paper, reliability of LPS was estimated with subsystem test data using computational Bayesian approach. Component level, subsystem level and system level data are considered and a framework is created by combing all information. The reliability of the LPS was calculated using Markov Chain Monte Carlo (MCMC) simulation which has avoided numerical integration. Results are compared with Lindstorm Madden method and Bayesian hybrid method. Computational Bayesian approach can give reasonably better reliability estimate with limited test data.