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.


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    Titel :

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


    Beteiligte:
    Mykola Kulyk (Autor:in) / Sergiy Dmitriev (Autor:in) / Oleksandr Yakushenko (Autor:in) / Oleksandr Popov (Autor:in)


    Erscheinungsdatum :

    2013




    Medientyp :

    Aufsatz (Zeitschrift)


    Format :

    Elektronische Ressource


    Sprache :

    Unbekannt




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