This paper introduces a formal architecture for on-board diagnosis, prognosis and recovery called ARPHA. ARPHA is designed as part of the ESA/ESTEC study called VERIFIM (Veri\ufb01cation of Failure Impact by Model checking). The goal is to allow the design of an innovative on-board FDIR process for autonomous systems, able to deal with uncertain system/environment interactions, uncertain dynamic system evolution, partial observability and detection of recovery actions taking into account imminent failures. We show how the model needed by ARPHA can be built through a standard fault analysis phase, \ufb01nally producing an extended
version of a fault tree called EDFT; we discuss how EDFT can be adopted as a formal language to represent the needed FDIR knowledge, that can be compiled into a corresponding Dynamic Decision Network to be used for the analysis. We also discuss the software architecture we are implementing following this approach, where on-board FDIR can be implemented by exploiting on-line inference based on the junction tree approach typical of probabilisticgraphical models
