Using Dynamic Decision Networks and Extended Fault Trees for Autonomous Fault Detection, Identification and Recovery

We address the problem of defining the behavior of an autonoumous FDIR (Fault Detection, Identification and Recovery) agent (e.g. a space rover), in presence of uncertainty and partial observability; we show how a Dynamic Decision Network (DDN) can be built through a fault analysis phase by producing an Extended Dynamic Fault Tree (EDFT). In this fault tree extension, several modeling features are introduced: a generalization of Boolean components to multi-state components, general stochastic dependencies among components, and finally external actions on the system as well as controllable actions triggered by the system itself. We discuss how EDFT can be adopted as a formal modeling language (familiar to reliability engineers), then compiled into a DDN for the FDIR analysis through standard inference algorithms