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.


    Access

    Download


    Export, share and cite



    Title :

    Method of formulating input parameters of neural network for diagnosing gas-turbine engines


    Contributors:


    Publication date :

    2013




    Type of media :

    Article (Journal)


    Type of material :

    Electronic Resource


    Language :

    Unknown




    Fuzzy-neural Approach for Aircraft Gas Turbine Engines Diagnosing

    Pashayev, Arif / Askerov, Djahangir / Sadiqov, Ramiz et al. | AIAA | 2004


    AIAA-2004-6222 Fuzzy-neural Approach for Aircraft Gas Turbine Engines Diagnosing

    Abdullayev, P. / Pashayev, A. / Askerov, D. et al. | British Library Conference Proceedings | 2004



    Network Automatic Monitoring and Diagnosing System for Thermo Parameters of Dredger Disel Engines Based on Labview

    Tiehua, P. / Xinping, Y. / Chenxing, S. | British Library Online Contents | 2005


    Formulating Advanced 4 Centistoke Gas Turbine Oils-A Feasibility Study

    Thompson, Quentin E. / Zielinski, Ronald E. | SAE Technical Papers | 1985