Abstract
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.
Highlights
IntroductionA way to improve the efficiency and reliability of dia- the operational process obtained
The output of the model is the desired parameters ofA way to improve the efficiency and reliability of dia- the operational process obtained
The diagnostic paragnosis when assessing the technical conditions of the meters for training the network can be used as relative air-gas path of a gas-turbine engine is analysis of the diagnostic deviations of these parameters: parameters of operational process by using neural networks
Summary
A way to improve the efficiency and reliability of dia- the operational process obtained. The diagnostic paragnosis when assessing the technical conditions of the meters for training the network can be used as relative air-gas path of a gas-turbine engine is analysis of the diagnostic deviations of these parameters: parameters of operational process by using neural networks. The only real source of the required information can be the results of a numerical experiment by using a mathematical model of the operational process of a gas-turbine engine. This paper considers a method of obtaining test and training sets for a static neural network. Except for diagnostic deviations, a parameter characterising the operating conditions of a gas-turbine engine can be included in the set structure (revolutions per minute [rpm], for example). The parameters of diagnostic deviations that are measured in flight have been included in the data sets for this engine: rpm of a high pressure rotor nhp; total pressure behind the fan P*fan; total pressure behind the compressor Р*с; temperature behind the compressor Т*с; fuel consumption Gf; temperature behind the turbine T*t; and the relation of total pressure behind the turbine to atmospheric
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