Expert System for Identification of the Technical State of the Aircraft Engine TV3-117 in Flight Modes

Shmelov, Yurii; Vladov, Serhii; Klimova, Yana A.; Kirukhina, Marina

doi:10.1109/saic.2018.8516864

Cited by 10 publications

(3 citation statements)

References 1 publication

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“…5. The prospect for further research is the implementation of the results of the obtained studies, as well as the developed method for diagnostics (monitoring) helicopters turboshaft engines pre-surge status, into the onboard neural network expert system for integrated monitoring and operation control of helicopters turboshaft engines at helicopter flight mode [35].…”

Section: Discussionmentioning

confidence: 99%

Neural Network Method for Detecting and Diagnostics Helicopters Turboshaft Engines Surge at Flight Modes

Vladov,

Shmelov,

Yakovliev

et al. 2023

Computer Modeling and Intelligent Systems

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The work is devoted to the development of a neural network method for diagnostics (monitoring) helicopters turboshaft engines pre-surge status in real time (at helicopter flight mode). The developed method for helicopters turboshaft engines is based on a mathematical model of the time distribution of air pressure in the compressor during the transient process, which is construct on the basis of the classical theory of the movement of liquids and gases, taking into account the features of the thermogas-dynamic flow in the compressor of helicopters turboshaft engines. Diagnostics (monitoring) helicopters turboshaft engines in real time is carried out using a linear neural network with the optimal number of neurons -10 or more. It is proposed to train a linear neural network on dynamic neurons, which, due to the adjustable smoothing parameter from the range from zero to one, made it possible to obtain an accuracy of 99.99 % of the task being solved. The developed method can serve as one of the blocks of the onboard neural network expert system for the integrated monitoring and operation of helicopters turboshaft engines, which automatically decides on helicopters turboshaft engines operational status at helicopter flight mode and provides the crew with information about the possibility of helicopter further movement.

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Section: Discussionmentioning

confidence: 99%

Neural Network Method for Detecting and Diagnostics Helicopters Turboshaft Engines Surge at Flight Modes

Vladov,

Shmelov,

Yakovliev

et al. 2023

Computer Modeling and Intelligent Systems

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“…The results of this work can be introduced into an intelligent on-board system for control and diagnosing of aircraft GTEs technical state, including TV3-117 [24].…”

Section: Discussionmentioning

confidence: 99%

Control and Diagnostics of TV3-117 Aircraft Engine Technical State in Flight Modes Using the Matrix Method for Calculating Dynamic Recurrent Neural Networks

Vladov¹,

Shmelov²,

Yakovliev³

2021

Computer Modeling and Intelligent Systems

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In this paper, one of the methods of using dynamic recurrent neural networks for solving applied problems of control and diagnosing of aircraft engines technical state, including TV3-117, is considered. A method for coding neural networks into signal graphs is proposed, and it is shown that their adjacency matrices can be used as associative memory in step matrix algorithms for solving dynamic recurrent neural networks. It is shown that in fully connected recurrent neural networks, any neuron can be input or output, and one neuron can simultaneously be input and output. Examples of teaching by the evolutionary optimization algorithm for multiextremal problems of recurrent dynamic neural networks intended for control and diagnosing of TV3-117 aircraft engine technical state are given. The functions of activation blocks of neurons in dynamic recurrent neural networks in this work are used difference expressions of simulation models of linear dynamic links. It is shown that for identification in the time domain of transient processes in dynamic systems of the third order, satisfactory accuracy is achieved at the output of any neuron of a recurrent dynamic neural network with four neurons, while it was found that useful information about the dynamic properties of the dynamic system under study can be simultaneously obtained from the output of any neuron network.

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“…To teach a neuro-emulator, a multilayered direct-propagation network with randomly selected weights and a training set consisting of the pairs of the network inputthe desired output {X, D}, as well as the output value of the network Y, are determined. To find the minimum and determine the weight coefficients that are included in the     , N jp yx function of the method of fastest descent [18], in which at each step of the training change the weight coefficients in accordance with the expression:…”

Section: Fig 3 Structure Of the Generalized Neural Networkmentioning

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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Expert System for Identification of the Technical State of the Aircraft Engine TV3-117 in Flight Modes

Cited by 10 publications

References 1 publication

Neural Network Method for Detecting and Diagnostics Helicopters Turboshaft Engines Surge at Flight Modes

Neural Network Method for Detecting and Diagnostics Helicopters Turboshaft Engines Surge at Flight Modes

Control and Diagnostics of TV3-117 Aircraft Engine Technical State in Flight Modes Using the Matrix Method for Calculating Dynamic Recurrent Neural Networks

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

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