Development of a method for optimizing the structure of static neural networks intended for categorizing technical state of gas-turbine engines

Abstract

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.

Implementation of the mathematical model of interaction of passenger transport modes for sustainable economic development

“…Problems of transport modelling were considered in particular by Antonova et al [5; 6], Yakushenko [7] and Shevchuk [8].…”