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Proceedings Paper

Failure prediction in monitoring and control systems using Bayesian networks
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Paper Abstract

Predicting failure in complex systems, such as satellite network systems, is a challenging problem. A satellite earth terminal contains many components, such high-powered amplifiers, signal converters, modems, routers, and generators, any of which may cause system failure. The ability to estimate accurately the probability of failure of any of these components, given the current state of the system, may help reduce the cost of operation. Probabilistic graphical models, in particular Bayesian networks, provide a consistent framework in which to address problems containing uncertainty and complexity. Measurable nodes of the Bayesian network correspond to states of measurable parameters in the system and unmeasurable nodes represent failure of various components. Nodes for environmental factors are also included. A description of Bayesian networks will be provided and a demonstration of inference on the Bayesian network, such as the calculation of the marginal probability of failure nodes given measurements and the maximum probability state of the system for failure diagnosis will be given.

Paper Details

Date Published: 20 September 2007
PDF: 9 pages
Proc. SPIE 6695, Optics and Photonics for Information Processing, 66950U (20 September 2007); doi: 10.1117/12.734984
Show Author Affiliations
Steven Bottone, DataPath, Inc. (United States)
Clay Stanek, DataPath, Inc. (United States)


Published in SPIE Proceedings Vol. 6695:
Optics and Photonics for Information Processing
Abdul A.S. Awwal; Khan M. Iftekharuddin; Bahram Javidi, Editor(s)

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