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
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;
Conference_Titel :
Control Applications, 1999. Proceedings of the 1999 IEEE International Conference on
Conference_Location :
Kohala Coast, HI
Print_ISBN :
0-7803-5446-X
DOI :
10.1109/CCA.1999.806140
