A Bayesian Networks Approach to Reliability Analysis of a Launch Vehicle Liquid Propellant Engine

This paper presents an extension of Bayesian networks (BN) applied to reliability analysis of an open cycle gas generator liquid propellant engine (OGLE) of launch vchichs. There are several methods for system reliability analysis such as BBD, FTA, FMEA, Markov Chains, etc. But for complex systems such as a Launch Vechicle (LV), they are not all efficiently applicable due to failure dependencies between componemts, computational complexity and state space explosion problems. Thus, to overcome these problems, the BN modeling is pre;fe;rred for OGLE reliability analysis. In this algorithm, first, the functional models of OGLE are constructed based on expert knowledge and experiments involving system and subsystems interactions. Them, failure modes are derived through performing FMEA. Furthermore, by using modeling properties of Bayesian networks, a constructional model for failure propagation is obtained based on the acquired functional model and FMEA. Finally, by allocating quantitative properties to the Bayesian model and its inference, the reliability of OGLE is obtained. The results are verified using the Monte Carlo simulation results. Comparing the values obtained from the two applied methods shows the high accuracy and efficiency of the introduced algorithm for reliability analysis of OGLE and other complex systems with dependant failure modes in LV.