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developing large-scale bayesian networks by composition: fault diagnosis of electrical power systems in aircraft and spacecraftThis CD contains files that support the talk (see CASI ID 20100021404). There are 24 models that relate to the ADAPT system and 1 Excel worksheet. In the paper an investigation into the use of Bayesian networks to construct large-scale diagnostic systems is described. The high-level specifications, Bayesian networks, clique trees, and arithmetic circuits representing 24 different electrical power systems are described in the talk. The data in the CD are the models of the 24 different power systems.
Document ID
20100021910
Document Type
Other - Data Set
Authors
Mengshoel, Ole Jakob
(Carnegie-Mellon Univ. Pittsburgh, PA, United States)
Poll, Scott
(NASA Ames Research Center Moffett Field, CA, United States)
Kurtoglu, Tolga
(Mission Critical Technologies, Inc. Moffett Field, CA, United States)
Date Acquired
August 24, 2013
Publication Date
July 13, 2009
Subject Category
Aircraft Propulsion and Power
Report/Patent Number
ARC-E-DAA-TN697
Meeting Information
Twenty-first International Joint Conference on Artificial Intelligence(Pasadena, CA)
Funding Number(s)
CONTRACT_GRANT: NNX08AY50A
Distribution Limits
Public
Copyright
Public Use Permitted.

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IDRelationTitle20100021404Supplement ToDeveloping Large-Scale Bayesian Networks by Composition: Fault Diagnosis of Electrical Power Systems in Aircraft and Spacecraft20100021404See AlsoDeveloping Large-Scale Bayesian Networks by Composition: Fault Diagnosis of Electrical Power Systems in Aircraft and Spacecraft