In this paper we present a new architecture for integrating system health monitoring tasks into the development- and life cycle of space systems. On the basis of model-supported diagnosis technology the presented method uses information for diagnosis purposes that is already gathered during the development of a technical system. This information is extracted from simulation models used for design-studies and what-if-analyses during the design- and development phase.
For building up these simulation models easily, we developed a library of generic models of spacecraft components. These models cover the components' nominal and off-nominal behavior as it is specified in the component FMECAs. By combining and parametrizing the components a system model is built up. Since due to the limited resources on board of a spacecraft we can not use the model directly for model-based diagnosis, we use a model-supported approach: By systematically simulating possible component faults within the system's operational modes, we retrieve a set of measurement data that serve as symptoms to the failure modes. By classifying these data we get a knowledge-base for a symptom-based on-board diagnosis system. In order to cope with the uncertainty in the measurement data, this diagnosis system has been realized as a fuzzy system that on the basis of the given knowledge-base computes the most probable diagnoses from the given symptoms. The described system has been implemented within Astrium's Columbus Simulation System (CSS) and has been evaluated on several aerospace systems ranging from an unmanned aerial robot on the basis of an airship to the Propulsion and Reboost Subsystem of the Automated Transfer Vehicle (ATV), a supply spacecraft for the International Space Station.