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

Oleksandr Yakushenko,Oleg Chumak,Azer Mirzoyev,Oleksandr Popov,Valerii Okhmakevych

doi:10.15587/1729-4061.2020.218137

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 14.5 %

Highlights

One of the ways to improve the quality and efficiency of diagnosing gas turbine engines (GTE) implies an automated analysis of functioning parameters implemented in the form of a computer diagnostic system
The study objective is to devise a method for preparing a static neural network (NN) intended for the classification of aircraft GTE technical state (TS) according to the parameters measured in operation
If the neural network which did not provide the appearance of the overlearning effect was used at Stage 3, the hypothesis of the neural network size redundancy is confirmed if the neural network selected at Stage 4 is much simpler than that used at Stage 3

Summary

Introduction

One of the ways to improve the quality and efficiency of diagnosing gas turbine engines (GTE) implies an automated analysis of functioning parameters implemented in the form of a computer diagnostic system. The fact that the algorithm of information processing inside the NN is usually a black box even for the method developer is one of the features of parametric diagnostics using NNs. At the same time, the developer collects the data necessary for training the neural network, sets its general structure, trains the network, and monitors the results obtained. The network under consideration provides a classification of the GTE TS according to the results of processing the parameters measured at separate, unrelated points in time. In this case, pre-classified samples are used (training with a “trainer”). This will result in the growth of the flight safety level

Literature review and problem statement

The aim and objectives of the study

The results of neural network optimization

Conclusions