Spacecraft on-board real time software architecture for fault detection and identification

Spacecraft on-board computer (OBC) software architecture is composed of modules consolidated to satisfy the mission requirements and maintains spacecraft operation with high reliable capabilities in existence of malfunction. In order to increase spacecraft OBC software reliability and then increase spacecraft autonomy, it is crucial to impose a centralized fault detection and identification (FDI) functionality into the onboard software to detect and classify spacecraft anomalies in order to be recovered by ground control station. The aim of this research is to introduce real time software architecture with an accurate and efficient FDI technique for spacecraft. This approach uses Prony approximation and feed forward neural network (FFNN) to differentiate among anomalies that occur in spacecraft reaction wheels and solar panels. Results verify that the proposed architecture with FDI technique is successfully validated complying with real time software constraints for spacecraft OBC.