Method of formulating input parameters of neural network for diagnosing gas-turbine engines
DOI: https://doi.org/10.3846/16487788.2013.805868Abstract
A method of obtaining test and training data sets has been developed. These sets are intended for training a static neural network to recognise individual and double defects in the air-gas path units of a gas-turbine engine. These data are obtained by using operational process parameters of the air-gas path of a bypass turbofan engine. The method allows sets that can project some changes in the technical conditions of a gas-turbine engine to be received, taking into account errors that occur in the measurement of the gas-dynamic parameters of the air-gas path. The operation of the engine in a wide range of modes should also be taken into account.
First published online: 01 Jul 2013
Keywords:
gas-turbine engine, air-gas path, mathematical model of operational process, neural networkHow to Cite
Share
License
Copyright (c) 2013 The Author(s). Published by Vilnius Gediminas Technical University.
This work is licensed under a Creative Commons Attribution 4.0 International License.
View article in other formats
Published
Issue
Section
Copyright
Copyright (c) 2013 The Author(s). Published by Vilnius Gediminas Technical University.
License
This work is licensed under a Creative Commons Attribution 4.0 International License.