Date of Original Version
1-1-2008
Type
Article
Abstract or Table of Contents
Electrical power systems play a critical role in spacecraft and aircraft. This paper discusses our development of a diagnostic capability for an electrical power system testbed, ADAPT, using probalistic techniques. In the context of ADAPT, we present two challenges, regarding modelling and real-time performance, often encountered in real-world diagnostic applications. To meet the modelling challenge, we discuss our novel high-level specification language which supports auto-generation of Bayesian networks. To meet the real-time challenge, we compile Bayesian networks intro arithmetic circuits. Arithmetic circuits typically have small footprints and are optimized for the real-time avionics systems found in spacecraft and aircraft. Using our approach, we present how Bayesian networks with over 400 nodes are auto-generated and then compiled into arithmetic circuits. Using real-time world data from ADAPT as well as simulated data, we obtain average inference times smaller than one millisecond when computing diagnostic queries using arithmetic circuits that model our real world electrical power system.
Included in
Computer and Systems Architecture Commons, Computer Sciences Commons, Hardware Systems Commons, Other Electrical and Computer Engineering Commons, Systems and Communications Commons, VLSI and circuits, Embedded and Hardware Systems Commons
Comments
Copyright 2008. Association for the Advancement of Artificial Intelligence (www.aaai.org). All rights reserved.