DocumentCode
3334154
Title
Bayesian belief networks for fault identification in aircraft gas turbine engines
Author
Mast, Timothy A. ; Reed, Aaron T. ; Yurkovich, Stephen ; Ashby, Malcolm ; Adibhatla, Shrider
Author_Institution
Ohio State Univ., Columbus, OH, USA
Volume
1
fYear
1999
fDate
1999
Firstpage
39
Abstract
Describes the methodology for usage of Bayesian belief networks (BBNs) in fault detection for aircraft gas turbine engines. First, the basic theory of BBNs is discussed, followed by a discussion on the application of this theory to a specific engine. In particular, the selection of faults and the means by which operating regions for the BBN system are chosen are analyzed. This methodology is then illustrated using the GE CFM56-7 turbofan engine as an example
Keywords
aerospace engines; belief networks; fault diagnosis; gas turbines; Bayesian belief networks; GE CFM56-7 turbofan engine; aircraft gas turbine engines; fault identification; Aircraft propulsion; Bayesian methods; Data analysis; Fault detection; Fault diagnosis; Intelligent networks; Jet engines; Random variables; Turbines; Uncertainty;
fLanguage
English
Publisher
ieee
Conference_Titel
Control Applications, 1999. Proceedings of the 1999 IEEE International Conference on
Conference_Location
Kohala Coast, HI
Print_ISBN
0-7803-5446-X
Type
conf
DOI
10.1109/CCA.1999.806140
Filename
806140
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