Диагностирование Электромеханических Систем  На Основе Нейросетевых Технологий

Палюх, Б. В.; Palyukh, B. V.; Богатиков, В.Н.; Shprekher, D.M.; Sprekher, D.M.

doi:10.15827/0236-235x.111.005-011

Cited by 3 publications

(1 citation statement)

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“…The neural network is capable of performing various actions, such as control of the dynamic objects [4,6,[10][11][12][13], equipment diagnostics [8,[14][15][16], forecasting of the production situations, monitoring of the technological processes.…”

Section: Introductionmentioning

confidence: 99%

Evaluating the Possibilities of Applying an Artificial Neural Network for Control and Diagnostics of the Electric Drive Systems

Baghdasaryan

Hovhannisyan

Hakobyan

2022

Electrical Engineering, Energetics

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The paper investigates and evaluates the possibilities of using an artificial neural network. Attention is paid to the structural and operational features of the artificial neural networks, the learning processes used in them and their capabilities. The methods of application of artificial neural network for the purpose of control and diagnostics of dynamic systems of the electric drives are considered. A comparative analysis of the electric drive systems with PID regulators and neuroregulators was conducted. The expediency and necessity of improving the abilities of training artificial neural networks for adaptive control and diagnostics of the electric drives with incomplete description have been revealed, including the drives operating under random influences and dynamically changing modes. The main circumstances preventing the use of artificial neural networks, the laws of the choice of types and methods of optimization in the process of artificial neural networks (ANN) training and the lack of criteria for choosing the number of the neurons in the network are given. A review of well-known works devoted to the use of ANN in the electric drive systems, as well as a comparative analysis of the feasibility of using gradient and genetic methods of their training are carried out. The comparative analysis was carried out by summarizing the conclusions in various published works. Analysis shows that, in most cases, networks trained by genetic algorithms provide more accurate results, easier learning, and shorter duration. At the same time, in some cases, the use of the back propagation algorithm in the certain problems leads to better results. Thus, it can be stated that the use of the preferred algorithm depends on the formulation of the task.

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Section: Introductionmentioning

confidence: 99%

Evaluating the Possibilities of Applying an Artificial Neural Network for Control and Diagnostics of the Electric Drive Systems

Baghdasaryan

Hovhannisyan

Hakobyan

2022

Electrical Engineering, Energetics

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Scientific Substantiation of Topological Diagnostics Methods of Electrical Equipment

Kurilin

Fedulov

Sokolov

2021

2021 International Conference on Industrial Engineering, Applications and Manufacturing (ICIEAM)

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The Algorithm for Monitoring the Asymmetric Modes of a Three-Phase Synchronous Machine

Baghdasaryan¹,

Arakelyan²,

DAVTYAN³

2022

Proceedings

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The possibilities of monitoring and detecting malfunctions of a synchronous machine are analyzed. The necessity of using the most effective algorithm, which provides the possibility of analyzing a large number of data for monitoring the asymmetric modes of a synchronous machine in the case of phase short circuits is substantiated. An algorithm for monitoring the occurrence of asymmetric modes in a synchronous machine and a block diagram of the system have been developed.

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Диагностирование Электромеханических Систем На Основе Нейросетевых Технологий

Cited by 3 publications

References 0 publications

Evaluating the Possibilities of Applying an Artificial Neural Network for Control and Diagnostics of the Electric Drive Systems

Evaluating the Possibilities of Applying an Artificial Neural Network for Control and Diagnostics of the Electric Drive Systems

Scientific Substantiation of Topological Diagnostics Methods of Electrical Equipment

The Algorithm for Monitoring the Asymmetric Modes of a Three-Phase Synchronous Machine

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