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

Vitalii Emelianov, Sergei Chernyi, Anton Zinchenko, Nataliia Emelianova, Elena Zinchenko and Kirill Chernobai
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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
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
Kirill Chernobai: Department of Cyber-Physical Systems, St. Petersburg State Marine Technical University, 190121 St. Petersburg, Russia

Energies, 2022, vol. 15, issue 9, 1-12

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.

Keywords: information system; diagnosing; lining; steel ladle; neural network; software (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2022
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