Please use this identifier to cite or link to this item: http://er.nau.edu.ua/handle/NAU/38320
Title: METHOD OF FORMULATING INPUT PARAMETERS OF NEURAL NETWORK FOR DIAGNOSING GAS-TURBINE ENGINES
Authors: Kulyk, Mykola
Dmitriev, Sergiy
Yakushenko, Oleksandr
Popov, Oleksandr
Keywords: gas-turbine engine
air-gas path
mathematical model of operational process
neural network
Issue Date: May-2013
Publisher: AVIATION. Taylor&Francis
Series/Report no.: 17(2) 2013;
Abstract: A method of obtaining test and training data sets has been developed. 弻ese sets are intended for train- ing a static neural network to recognise individual and double defects in the air-gas path units of a gas-turbine engine. 弻ese data are obtained by using operational process parameters of the air-gas path of a bypass turbofan engine. 弻e 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. 弻e op- eration of the engine in a wide range of modes should also be taken into account
URI: http://er.nau.edu.ua/handle/NAU/38320
ISSN: 1648-7788 print / ISSN 1822-4180
Appears in Collections:Наукові статті кафедри авіаційних двигунів

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