The goal of this project is to provide self-diagnostic capabilities to the thermal protection systems (TPS) of future spacecraft. Self-diagnosis is especially important in thermal protection systems (TPS), where large numbers of parts must survive extreme conditions after weeks or years in space. In-service inspections of these systems are difficult or impossible, yet their reliability must be ensured before atmospheric entry. In fact, TPS represents the greatest risk factor after propulsion for any transatmospheric mission. The concepts and much of the technology would be applicable not only to the Crew Exploration Vehicle (CEV), but also to ablative thermal protection for aerocapture and planetary exploration. Monitoring a thermal protection system on a Shuttle-sized vehicle is a daunting task: there are more than 26,000 components whose integrity must be verified with very low rates of both missed faults and false positives. The large number of monitored components precludes conventional approaches based on centralized data collection over separate wires; a distributed approach is necessary to limit the power, mass, and volume of the health monitoring system. Distributed intelligence with self-diagnosis further improves capability, scalability, robustness, and reliability of the monitoring subsystem. A distributed system of intelligent sensors can provide an assurance of the integrity of the system, diagnosis of faults, and condition-based maintenance, all with provable bounds on errors
