Share Email Print

Proceedings Paper

Failure prediction for satellite monitoring systems using Bayesian networks
Author(s): Steven Bottone; Daniel Lee; Clay Stanek; Michael O'Sullivan; Mark Spivack
Format Member Price Non-Member Price
PDF $14.40 $18.00
cover GOOD NEWS! Your organization subscribes to the SPIE Digital Library. You may be able to download this paper for free. Check Access

Paper Abstract

Predicting failure in complex systems, such as satellite network systems, is a challenging problem. A satellite earth terminal contains many components, including 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. Building a Bayesian network for failure prediction in a complex system such as a satellite earth terminal requires a large quantity of data. Software monitoring systems have the potential to provide vast amounts of data related to the operating state of the satellite earth terminal. 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. Using the data to learn local probabilities of the network will also be covered.

Paper Details

Date Published: 5 October 2007
PDF: 12 pages
Proc. SPIE 6736, Unmanned/Unattended Sensors and Sensor Networks IV, 67360G (5 October 2007); doi: 10.1117/12.738114
Show Author Affiliations
Steven Bottone, DataPath, Inc. (United States)
Daniel Lee, DataPath, Inc. (United States)
Clay Stanek, DataPath, Inc. (United States)
Michael O'Sullivan, San Diego State Univ. (United States)
Mark Spivack, Univ. of Cambridge (United Kingdom)

Published in SPIE Proceedings Vol. 6736:
Unmanned/Unattended Sensors and Sensor Networks IV
Edward M. Carapezza, Editor(s)

© SPIE. Terms of Use
Back to Top