Image Processing for Technological Diagnostics of Metallurgical Facilities

Doukovska, Lyubka; Petkov, Venko; Mihailov, Emil; Vassileva, Svetla

doi:10.2478/cait-2012-0031

Cited by 14 publications

(6 citation statements)

References 6 publications

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“…There are some papers [9][10][11][12][13] in the field of automation of monitoring the ladle cars state. In paper [14], an infrared system is proposed for diagnosing the lining of a ladle.…”

Section: Literature Reviewmentioning

confidence: 99%

Application of Artificial Intelligence Technologies for Diagnostics of Production Structures

Chernyi

Emelianov

Zinchenko³

et al. 2022

JMSE

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The paper presents that during the operation of torpedo ladle cars in metallurgical production, problems periodically arise with ensuring the safety of their use. The authors have highlighted the relevance and necessity of the solution to the problem of diagnosing the lining state of ladle cars to ensure their safe functioning. To solve the problem of diagnosing the lining state of ladle cars for the maritime industry, an algorithm for detecting burnout zones of a lining based on a neural network has been developed. The authors propose and describe a distributed multi-agent information control system for the operation of torpedo ladle cars. The results for detecting burnout zones of a lining by the standard system and newly developed system are presented. To automate assessing the lining state of the ladle car and support in making decisions regarding operation mode of the ladle cars, the software has been developed.

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Section: Literature Reviewmentioning

confidence: 99%

Application of Artificial Intelligence Technologies for Diagnostics of Production Structures

Chernyi

Emelianov

Zinchenko³

et al. 2022

JMSE

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“…У полированной (зеркальной) поверхности излучательная способность ниже 0,1, в то время как у окисленной или окрашенной поверхности -0,85…0,95. Например, краска на масляной основе, независимо от цвета в видимом спектре, имеет излучательную способность более 0,9 в инфракрасном диапазоне [1,2,21,22].…”

Section: Fig 4 System Model With Simplified Accounting Of Ems Mechaunclassified

Diagnostics of Thermal Condition of Electromechanical Machinery

Borisenko

Zemlyansky

Сидоров

et al. 2020

Gorn. nauki tehnol.

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Аннотация: В работе рассмотрены вопросы контроля теплового состояния объектов промышленной группы производственных механизмов на основе обобщенного подхода к пониманию причин и целей диагностирования электромеханических систем (ЭМС) по текущим значениям температуры. Обобщенная математическая модель ЭМС (однородного тела или многомассовой расчетной тепловой схемы замещения) для различных режимов работы объекта позволяет обозначить диагностические признаки (критерии) для принятия конкретных мер по стабилизации его работы. Повышение эффективности контроля теплового состояния объектов можно достичь при использовании приборов бесконтактного измерения распределения температур по всей поверхности объекта. По виду распределения температур по поверхности объектов ЭМС можно сделать вывод о необходимости проведения ремонтных работ. Для эффективного использования бесконтактной тепловизионной техники для диагностики ЭМС на основе изложенных в статье положений разработана программа подготовки специалистов по термометрированию промышленных объектов. Ключевые слова: тепловой контроль, электромеханическая система, тепловая расчетная масса, распределение температур, диагностический признак.

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“…Lined equipment, including torpedo ladle cars, hot-metal cars and steel ladles are considered to be critical equipment at heavy industry and machinery building facilities. Such equipment is classified as critical since consequences of accidents involving such equipment lead to multimillion losses for the enterprise and in some cases to human deaths, which is proved by a number of sources [1][2][3]. In order to prevent accidents with such type of equipment and maintain industrial safety at the production facility, a growing number of diagnostic operations and technologies to control technical condition of lined equipment are applied in production units which, in turn, require development of new and improvement of existing technical means and information technologies Nowadays there are different automated systems to monitor, diagnose and operate critical equipment at production units [4][5][6].…”

Section: Introductionmentioning

confidence: 99%

Neural network to diagnose lining condition

Yemelyanov

Yemelyanova

Nedelkin

et al. 2018

IOP Conf. Ser.: Mater. Sci. Eng.

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Abstract. The paper presents data on the problem of diagnosing the lining condition at the iron and steel works. The authors describe the neural network structure and software that are designed and developed to determine the lining burnout zones. The simulation results of the proposed neural networks are presented. The authors note the low learning and classification errors of the proposed neural networks. To realize the proposed neural network, the specialized software has been developed.

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Image Processing for Technological Diagnostics of Metallurgical Facilities

Cited by 14 publications

References 6 publications

Application of Artificial Intelligence Technologies for Diagnostics of Production Structures

Application of Artificial Intelligence Technologies for Diagnostics of Production Structures

Diagnostics of Thermal Condition of Electromechanical Machinery

Neural network to diagnose lining condition

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