  We demonstrate an end-to-end framework to improve the resilience of man-made
systems to unforeseen events. The framework is based on a physics-based digital
twin model and three modules tasked with real-time fault diagnosis, prognostics
and reconfiguration. The fault diagnosis module uses model-based diagnosis
algorithms to detect and isolate faults and generates interventions in the
system to disambiguate uncertain diagnosis solutions. We scale up the fault
diagnosis algorithm to the required real-time performance through the use of
parallelization and surrogate models of the physics-based digital twin. The
prognostics module tracks the fault progressions and trains the online
degradation models to compute remaining useful life of system components. In
addition, we use the degradation models to assess the impact of the fault
progression on the operational requirements. The reconfiguration module uses
PDDL-based planning endowed with semantic attachments to adjust the system
controls so that the fault impact on the system operation is minimized. We
define a resilience metric and use the example of a fuel system model to
demonstrate how the metric improves with our framework.

Artificial Intelligence;Systems and Control;Systems and Control