Diagnostic Methods for an Aircraft Engine Performance

Ntantis, Efstratios L.; Botsaris, Pantelis N.

doi:10.25103/jestr.084.10

Cited by 35 publications

(30 citation statements)

References 56 publications

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“…Despite the considerable amount of research in this field [1][2][3], information technology for control and diagnostics of the technical condition of aviation engines is not perfect for a number of reasons: on the one hand, weak information "link", lack of elements of "intelligence", allowing to rapidly, efficiently and effectively support responsible decision-making and, as a consequence, reduce the total time spent on maintenance of aviation engines; on the other hand, the unsteadiness of physical processes in the aviation engine, the complexity of their mathematical description, the dependence of its technical characteristics on external conditions of work, the limited composition of the measured parameters, their technological spread, etc. These factors lead to the need to automate the decision-making process on the technical condition of the aircraft engine under uncertainty.…”

Section: Problem Statementmentioning

confidence: 99%

Neuro-mechanical methods of control and diagnostics of the technical state of aircraft engine TV3-117 in film regions

Vladov¹,

Kotliarov²,

Hrybanova³

et al. 2020

Вісник Херсонського національного технічного університету

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The subject of the study in the article is the modes of operation of the aircraft engine TV3-117 and methods of their control and diagnostics. The purpose of the work is to develop methods of control and diagnostics of the technical condition of the aircraft engine TV3-117 on the basis of neural network technologies in real time. The following tasks are solved: substantiation of the preconditions of the use of neural networks in the task of control and diagnostics of the technical condition of the aircraft engine TV3-117, construction of the generalized neural network and the choice of the algorithm for its training, the solution of the task of controlling the parameters of the technical condition of the aircraft engine TV3-117 with the use of neural networks. The following methods are used: methods of probability theory and mathematical statistics, methods of neuroinformatics, methods of the theory of information systems and data processing. The following results were obtained: The feasibility of using neural networks in the task of controlling and diagnosing the technical condition of the aircraft engine TV3-117 was substantiated. The expediency of developing neural networks based on the NN Predictive Controller. The expediency of using the gradient method of teaching neural networks is substantiated, as well as the method of training a neuro-regulator based on a neuro-modulator with the use of the method of reverse error propagation. The expediency of using the gradient method of teaching neural networks is substantiated, as well as the method of training a neuro-regulator based on a neuromodulator with the use of the method of reverse error propagation. The solution of the task of controlling the parameters of the technical condition of the aircraft engine ТВ3-117, which confirms the expediency of using neural networks in the task of control and diagnostics of the technical condition of the aircraft engine TV3-117, is obtained. Conclusions: The application of neural network technologies is effective in solving a wide range of poorly formalized tasks, one of which is the task of controlling the technical condition of the aircraft engine TV3-117. The advantage of neural networks in their application in the tasks of control and diagnostics of the technical condition of the aircraft engine TV3-117 is the possibility of working with small training samples, the appointment of soft tolerances, using the experience of experts to assess the technical condition of the aircraft engine TV3-117, which is important in the condition's information incompleteness.

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Section: Problem Statementmentioning

confidence: 99%

Neuro-mechanical methods of control and diagnostics of the technical state of aircraft engine TV3-117 in film regions

Vladov¹,

Kotliarov²,

Hrybanova³

et al. 2020

Вісник Херсонського національного технічного університету

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“…Analysis of works in the field of control and diagnostics of the state of aviation engines on the basis of neural networks [2][3][4][5][6] shows that at present, such works are being conducted, however, due to a number of reasons (secrecy, narrow specialization of the tasks to be solved), in most publications there are no engineering methods, as well as theoretical and practical recommendations for solving similar problems. The task of the problem and possible algorithms for choosing the architecture of neural networks, their algorithms, evaluation of their work efficiency, etc.…”

Section: Introductionmentioning

confidence: 99%

Identification of Rear Model of Tv3-117 Aircraft Engine Based on the Basis of Neuro-Multi-Functional Technologies

Vladov

Shmelov

Derevyanko

et al. 2019

Innovative Technologies and Scientific Solutions for Industries

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The subject matter in the article is TV3-117 aircraft engine and methods of identification of its technical condition. The goal of the work is to develop methods for identifying the technical state of the aircraft engine TV3-117 on the basis of real-time neural network technologies. The following tasks were solved in the article: the task of identifying the reverse multi-mode model of the aircraft engine TV3-117 using neural networks. The following methods used aremethods of probability theory and mathematical statistics, methods of neuroinformatics, methods of the theory of information systems and data processing. The following results were obtained-The application of the neural network apparatus is effective in solving a large range of tasks: identifying the mathematical model of the aircraft engine TV3-117, diagnosing the condition, analyzing the trends, forecasting the parameters, etc., despite the fact that these tasks usually relate to the class difficultly formalized (poorly structured), neural networks are adequate and effective in the process of their solution. In the process of solving the task of identifying the mathematical model of the aircraft engine TV3-117 on the basis of neural networks, it was established that neural networks solve the problem of identification more precisely classical methods. Conclusions: It was established that the error of identification of the aircraft engine TV3-117 with the help of a neural network of type perceptron did not exceed 1.8 %; For the neural network of radial-basic function (RBF)-4.6 %, whereas for the classical method (LSM) it makes about 5.7 % in the considered range of changes in engine operation modes. It was found that neural network methods are more robust to external perturbations: for noise level ζ = 0.01, the error of identification of aircraft engine TV3-117 with the use of perceptron has increased from 1.8 to 3.8 %; for the neural network RBFfrom 4.6 to 5.7 %, and for the least squares methodfrom 5.7 to 13.93 %. In the process of solving the task of identifying the inverse multi-mode model of the aviation engine TV3-117 on its parameters on the basis of neural networks (perceptron and RBF) it was shown that their use allows for indirect measurement of the parameters of the flowing part of the engine at different modes of its operation: in the absence of noisewith an error of not more than 1,8 and 4,6 % respectively; in the presence of noise (ζ = 0,01)with an error of not more than 3,8 and 5,7 % respectively. Application in these conditions of the least squares method (polynomial regression model of the 8th order) allows us to obtain the error value: in the absence of noiseno more than 5,7 %; in the presence of noiseno more than 13,93 %.

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“…These defects can be damage to the turbine blades, combustion chamber disturbance, disturbance to the homogeneous field of fuel burning, etc. [28].Aircraft engine diagnostics and health monitoring [29][30][31] can be divided into two basic processes: ground diagnostics and on-board diagnostics with their associated systems. On-board, or online, diagnostics involve the checking of engine components and thermodynamic processes during the operation of the engine [32].…”

mentioning

confidence: 99%

Intelligent Thermal Imaging-Based Diagnostics of Turbojet Engines

et al. 2019

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There are only a few applications of infrared thermal imaging in aviation. In the area of turbojet engines, infrared imaging has been used to detect temperature field anomalies in order to identify structural defects in the materials of engine casings or other engine parts. In aviation applications, the evaluation of infrared images is usually performed manually by an expert. This paper deals with the design of an automatic intelligent system which evaluates the technical state and diagnoses a turbojet engine during its operation based on infrared thermal (IRT) images. A hybrid system interconnecting a self-organizing feature map and an expert system is designed for this purpose. A Kohonen neural network (the self-organizing feature map) is successfully applied to segment IRT images of a turbojet engine with high precision, and the expert system is then used to create diagnostic information from the segmented images. This paper represents a proof of concept of this hybrid system using data from a small iSTC-21v turbojet engine operating in laboratory conditions.

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Diagnostic Methods for an Aircraft Engine Performance

Cited by 35 publications

References 56 publications

Neuro-mechanical methods of control and diagnostics of the technical state of aircraft engine TV3-117 in film regions

Neuro-mechanical methods of control and diagnostics of the technical state of aircraft engine TV3-117 in film regions

Identification of Rear Model of Tv3-117 Aircraft Engine Based on the Basis of Neuro-Multi-Functional Technologies

Intelligent Thermal Imaging-Based Diagnostics of Turbojet Engines

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