Diagnosis of component failures in the Space Shuttle main engines using Bayesian belief networks: a feasibility study

Abstract

Although the Space Shuttle is a high reliability system, its condition must he accurately diagnosed in real-time. Two problems plague the system - false alarms that may be costly, and missed alarms which may be not only expensive, but also dangerous to the crew. This paper describes the results of a feasibility study in which a multivariate state estimation technique is coupled with a Bayesian belief network to provide both fault detection and fault diagnostic capabilities for the Space Shuttle main engines (SSME). Five component failure modes and several single sensor failures are simulated in our study and correctly diagnosed. The results indicate that this is a feasible fault detection and diagnosis technique and fault detection and diagnosis can he made earlier than standard redline methods allow.