Наукові статті кафедри авіаційних двигунів (НОВА)

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Browsing Наукові статті кафедри авіаційних двигунів (НОВА) by Author "Popov, O."

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    DEVELOPMENT OF A METHOD FOR OPTIMIZING THE STRUCTURE OF STATIC NEURAL NETWORKS INTENDED FOR CATEGORIZING TECHNICAL STATE OF GAS- TURBINE ENGINES
    (Eastern-European Journal of Enterprise Technologies, 2020-12-18) Yakushenko, O.; Popov, O.; Mirzoyev, A.; Chumak, O.; Okhmakevych, V.
    A process of creating a static neural network intended for diagnosing bypass gas turbine aircraft engines by a method of cat- egorizing 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 pro- vides 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 diag- nostics. 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 net- work 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 contain- ing measurement errors. A two-layer network with eight neurons in the first layer was obtained. The share of erroneous diagnoses measured 14.5 %.
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    Gas turbine engines diagnosing using the methods of pattern recognition
    (Авиационно–космическая техника и технология. Харьков., 2017-09) Dmitriev, S.; Popov, O.; Yakushenko, O.; Potapov, V.; Pashchuk, O.
    The paper is dedicated to the relevant problem that pertains to gas turbine engines diagnosing. The issue con- sidered in the paper is how to diagnose gas turbine engines using the methods of pattern recognition: in par- ticular the method of “binary tree” and the “nearest neighbor” method. In computer science, a binary tree is a tree data structure in which each node has at most two children, which are referred to as the left child and the right child. A recursive definition using just set theory notions is that a (non-empty) binary tree is a triple (L, S, R), where L and R are binary trees or the empty set and S is a singleton set. Some authors allow the binary tree to be the empty set as well. I n computing, binary trees are seldom used solely for their struc- ture. Much more typical is to define a labeling function on the nodes, which associates some value to each node. Nearest neighbor search (NNS), as a form of proximity search, is the optimization problem of finding the point in a given set that is closest (or most similar) to a given point. Closeness is typically expressed in terms of a dissimilarity function: the less similar th