Mixing Diagnosis Techniques for Autonomous Satellite FDIR

Abstract Thales Alenia Space is developing a new generation of satellites dedicated to flexible and autonomous Space missions. It relies on a hierarchical component based architecture. Hierarchical layers are devoted to manage on one hand, different kind of knowledge from abstract (goal of the mission) to real (units, equipments), and on the other hand, different reaction times, from orbit's order of magnitude (planning activities) to real time (thrusting, sensing). Components are most of the time related to functional chains covering pure discrete behavior (battery management) to pure continuous one (Guidance, Navigation and Control), or mix of both leading to hybrid modeling. Due to the vast range of models (discrete, continuous, hybrid – real time, no time constraint – teleological, functional, structural, behavioral), defining a FDIR (Fault Detection, Isolation and Recovery) strategy is a challenge. Solutions have been defined by making collaborate different diagnosis techniques (analytical redundancy, consistency based approach, model checking and Hinf estimator) from several origins, locally in each component. Abstraction of the local diagnosis results is translated at satellite level under the form of events and managed by a discrete automaton based formalism. Focus of the paper is put on the knowledge's modeling from the different layers of the architecture, the diagnosis technique in relation with the different kinds of components and the description of the hierarchical diagnosis solution.