Title :
Using Dynamic Decision Networks and Extended Fault Trees for Autonomous FDIR
Author :
Portinale, Luigi ; Codetta-Raiteri, Daniele
Author_Institution :
Dipt. di Inf., Univ. del Piemonte Orientale "A. Avogadro", Alessandria, Italy
Abstract :
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.
Keywords :
fault diagnosis; fault trees; inference mechanisms; EDFT; autonomous FDIR; dynamic decision networks; extended dynamic fault tree; fault detection, identification and recovery agent; fault tree extension; inference algorithms; Batteries; Fault trees; Logic gates; Power supplies; Reliability; Space vehicles; Stochastic processes; Autonomous Systems; Dynamic Bayesian Networks; Dynamic Decision Networks; FDIR;
Conference_Titel :
Tools with Artificial Intelligence (ICTAI), 2011 23rd IEEE International Conference on
Conference_Location :
Boca Raton, FL
Print_ISBN :
978-1-4577-2068-0
Electronic_ISBN :
1082-3409
DOI :
10.1109/ICTAI.2011.78