A process of creating a static neural network intended for diagnosing bypass gas turbine aircraft engines by a method of categorizing the technical state of the engine flow path was considered. Diagnostics depth was "to the structural assembly". A variant of diagnosing single faults of the flow path was considered.The following tasks were set:select the best neuron activation functions in the network layers;determine the number of layers; -determine the optimal number of neurons in layers;determine the optimal size of the training set.The problem was solved taking into account the influence of parameter measurement errors. The method of structure optimization implies training the network of the selected configuration using a training data set. The training was periodically interrupted to analyze the results of the network operation according to the criterion characterizing the quality of classification of the engine technical state. The assessment was performed with training and control sets. The network that provides the best value of the classification quality parameter assessed by the test set was selected as the final network. The PS-90A turbojet engine was selected as the object of diagnostics. Diagnostics was carried out on takeoff mode and during the initial climb. Primary optimization was carried out according to the data with no measurement errors. It was shown that a two-layer network with the use of neurons having a hyperbolic tangent function in both layers is sufficient to solve the problem. The size of the first network layer was finally optimized according to the data containing measurement errors.A two-layer network with eight neurons in the first layer was obtained. The share of erroneous diagnoses measured 14.
In this paper is shown the efficiency of the new Soft Computing technology application at different diagnosing stages of aviation gas turbine engine (GTE) technical condition with using Fuzzy Logic and Neural Networks methods, when the flight information has property of a fuzzy, limitation and uncertainty. On the fuzzy statistical data basis and with high accuracy is made the training of Fuzzy Multiple Linear and Non-Linear models (Fuzzy Regression Equations). Thus for GTE technical condition more adequate model making are analysed dynamics of skewness and kurtosis coefficients’ changes. Researches of skewness and kurtosis coefficients values’ changes show that, distributions of GTE work parameters have fuzzy character. Hence consideration of fuzzy skewness and kurtosis coefficients is expedient. Investigation of the basic characteristics changes’ dynamics of GTE work parameters allows to draw conclusion on necessity of the Fuzzy Statistical Analysis at preliminary identification of the engines’ technical condition. Researches of correlation coefficients values’ changes shows also on their fuzzy character. Therefore for models choice the application of the Fuzzy Correlation Analysis results is offered. For checking of models adequacy is considered the Fuzzy Multiple Correlation Coefficient of Fuzzy Multiple Regression. With a view of completeness of GTE technical condition diagnosing in this paper are considered Fuzzy Thermodynamic Models. As output parameter of these models the outlet gas temperature of gas turbine (turbine exhaust gas temperature -EGT) expediency is considered. In view of limitation of controllable parameters’ structure are used also semiempirical models. The developed GTE condition monitoring system provides stage-by-stage estimation of engine technical conditions. As application of the given technique the estimation of the new operating aviation engine temperature condition was made.
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