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Information System for Diagnosing the Condition of the Complex Structures Based on Neural Networks

Author

Listed:
  • Vitalii Emelianov

    (Financial University under the Government of the Russian Federation, 49 Leningradsky Prospekt, 125993 Moscow, Russia)

  • Sergei Chernyi

    (Department of Cyber-Physical Systems, St. Petersburg State Marine Technical University, 190121 St. Petersburg, Russia
    Complex Information Security Department, Admiral Makarov State University of Maritime and Inland Shipping, 198035 Saint-Petersburg, Russia
    Department of Ship’s Electrical Equipment and Automatization, Kerch State Maritime Technological University, 298309 Kerch, Russia)

  • Anton Zinchenko

    (Department of Cyber-Physical Systems, St. Petersburg State Marine Technical University, 190121 St. Petersburg, Russia)

  • Nataliia Emelianova

    (Financial University under the Government of the Russian Federation, 49 Leningradsky Prospekt, 125993 Moscow, Russia)

  • Elena Zinchenko

    (Department of Cyber-Physical Systems, St. Petersburg State Marine Technical University, 190121 St. Petersburg, Russia
    Department of Ship’s Electrical Equipment and Automatization, Kerch State Maritime Technological University, 298309 Kerch, Russia)

  • Kirill Chernobai

    (Department of Cyber-Physical Systems, St. Petersburg State Marine Technical University, 190121 St. Petersburg, Russia)

Abstract

In this paper, we describe the relevance of diagnosing the lining condition of steel ladles in metallurgical facilities. Accidents with steel ladles lead to losses and different types of damage in iron and steel works. We developed an algorithm for recognizing thermograms of steel ladles to identify burnout zones in the lining based on the technology and design of neural networks. A diagnostic system structure for automated evaluating of the technical conditions of steel ladles without taking them out of service has been developed and described.

Suggested Citation

  • Vitalii Emelianov & Sergei Chernyi & Anton Zinchenko & Nataliia Emelianova & Elena Zinchenko & Kirill Chernobai, 2022. "Information System for Diagnosing the Condition of the Complex Structures Based on Neural Networks," Energies, MDPI, vol. 15(9), pages 1-12, April.
    • Handle: RePEc:gam:jeners:v:15:y:2022:i:9:p:2977-:d:796934
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    References listed on IDEAS

    as
    1. Anton Zhilenkov & Sergei Chernyi & Vitalii Emelianov, 2021. "Application of Artificial Intelligence Technologies to Assess the Quality of Structures," Energies, MDPI, vol. 14(23), pages 1-12, December.
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