Моделирование системы искусственного интеллекта для опережающего выявления аварийных ситуаций на жизненно важных объектах
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Научный журнал Моделирование, оптимизация и информационные технологииThe scientific journal Modeling, Optimization and Information Technology
Online media
issn 2310-6018

Modeling of artificial intelligence system for early detection of emergency situations at vital facilities

idBorovskoy I.G. idShelmina E.A. Afanasyeva I.G.   idMatolygin A.A.

UDC 004.8
DOI: 10.26102/2310-6018/2022.38.3.001

  • Abstract
  • List of references
  • About authors

The article presents the results of modeling an artificial intelligence system for early detection of undesirable situations of various types at objects of particular national economic importance. Pipeline transport or any other production system, in which continuous monitoring of operability parameters of critical components and mechanisms is carried out, can be specified as such object. This model can be applied by various oil and gas production companies. The results of modeling and subsequent development of the information system will provide the basis for industrial implementation of highly effective systems of accident detection and prevention in reliance on neural network analysis of continuously received streaming data. As a part of this research, the possibility of using modern neural network architectures for the problem under consideration is examined, namely, convolutional neural networks – TCN, direct propagation neural networks – MLP, recurrent neural networks – LSTM. It was proposed to abandon the activation function for LSTM which helps to provide the neural network with "long-term memory" of stored values, which is crucial to this problem. In addition, a cross-comparison of the error reduction rate during network training was performed to detect an architecture capable of "self-learning". All models were tested with the aid of the training data from the "Vostochny kupol" wells. Acceptable coincidence of test and extrapolation data was obtained for all models.

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Borovskoy Igor Georgievich
Doctor of Physical and Mathematical Sciences Professor

WoS | Scopus | ORCID | eLibrary |

Tomsk State University of Control Systems and Radioelectronics

Tomsk, Russia

Shelmina Elena Aleksandrovna
Candidate of Physical and Mathematical Sciences

WoS | Scopus | ORCID | eLibrary |

Tomsk State University of Control Systems and Radioelectronics

Tomsk, Russia

Afanasyeva Inga Gennadievna

eLibrary |

Tomsk State University of Control Systems and Radioelectronics

Tomsk, Russia

Matolygin Andrey Anatolyevich

WoS | Scopus | ORCID | eLibrary |

Tomsk State University of Control Systems and Radioelectronics

Tomsk, Russia

Keywords: artificial intelligence, time series, artificial neural network, emergency, neural network architecture, convolutional neural networks, direct propagation neural networks, recurrent neural networks

For citation: Borovskoy I.G. Shelmina E.A. Afanasyeva I.G. Matolygin A.A. Modeling of artificial intelligence system for early detection of emergency situations at vital facilities. Modeling, Optimization and Information Technology. 2022;10(3). Available from: https://moitvivt.ru/ru/journal/pdf?id=1197 DOI: 10.26102/2310-6018/2022.38.3.001 (In Russ).

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Full text in PDF

Received 27.05.2022

Revised 28.06.2022

Accepted 13.07.2022

Published 13.07.2022