Intelligent and learning-based approaches for health monitoring and fault diagnosis of RADARSAT-1 attitude control system

The objective of this research is to develop to the proof-of-concept stage, a fault tolerant diagnosis system for the RADARSAT-1 attitude control system (ACS) telemetry. The proposed system is using computational intelligence (CI) to detect and isolate faults and also to infer cause of failures from the telemetry data time series history using functional models of satellite ACS. The proposed work is based on a distributed nonlinear, self-learning and self-adapting models (that can learn and improve themselves overtime) adjusting to the environment and constraints to which the real data is subjected. The key research and development issue is to create prototype models that will be able to integrate telemetry data and address the fault diagnosis problem without human intervention and expertise. The proposed work aims to support space industries´ future interests in on-board fault diagnosis for next generation spacecraft by utilizing CI techniques as well as to help ground system operators in performing calibrations, anomaly detection, isolation and recovery, or testing of components.