A failure diagnosis system based on a neural network classifier for the Space Shuttle main engine

A model-based failure diagnosis system based on a neural network classifier for the Space Shuttle main engine (SSME) is described. It relies on the accurate and reliable identification of the parameters of the highly nonlinear and very complex engine. The system may be used to monitor the life cycle of engine components and for the early detection, isolation, and diagnosis of engine failures. Thus the proposed system will be one part of a larger engine health monitoring system. The design approach is presented in some detail, along with the results for a failed valve. The preliminary results verify that the developed parameter identification technique, together with a neural network classifier, can be used for the detection and diagnosis of valve failure