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
