Current Space Shuttle main engine fault detection systems rely on sensor data analysis via redundant rule-based expert systems along with visual observations for the real-time assessment of engine health. A novel alternative to the traditional health-monitoring approach is predicated on the acquisition and subsequent neural network processing of electromagnetic plume emissions. Spectrometric examination of an emission spectrum provides a means for the identification and quantification of metallic species indigenous to the main engine plume flow. Knowledge of the metallic species eroding could pinpoint the specific location of component degradation within the engine, as well as identify serious component failures at an early stage. Such an approach is advantageous because it allows for the detection of numerous internal failures that would otherwise go unnoticed by traditional monitoring methods. A radial basis function neural network architecture that is capable of inferring metallic state from a given plume spectrum is detailed. Specifically, a comprehensive discussion of the methodologies necessary for the development and implementation of the neural network approach is provided. The resulting neural networks are validated with actual test-stand data from the January 1996 failure of a Space Shuttle main engine at NASA Stennis Space Center.
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