Izvekov Yu.A., Ramazanov K.N., Goncharova E.A., Karimova D.F., Shubin I.G., Smirnova L.V. Quality Management of Operation of Metallurgical Cranes Based on a Method of Pseudostates

DOI: 10.18503/1995-2732-2023-21-4-117-123

Abstract

Random process theory has an extensive field of engineering applications. A special place in this science is occupied by the so-called processes of death and reproduction. Despite the biological origin of this terminology of this theory, it immediately fits into existing technological processes. This paper is a continuation of studies aimed at an engineering and technological application of the theory of probabilities and random processes to build an efficient quality management system for hazardous production facilities of a metallurgical enterprise. When analyzing the operation of metallurgical cranes, we identify several states: operation, failure, repair and others. It is impossible to interrupt the production cycle in steelmaking divisions: if the crane fails, it must be replaced almost immediately. To provide rationale for technological solutions, we use the method of pseudostates of death and reproduction processes. Based on known approaches and models, we built a system of the Kolmogorov differential equations, and calculated the characteristics of the random process, mathematical expectation and dispersion. It is shown that probabilities of a steady state mode of a crane are distributed in pseudostates according to Poisson’s law. This shows the independence of the distribution of intervals between the events of registering cranes for replacement. The paper contains a hypothetical model example of calculating the random process characteristics of the number of operating units and their time characteristics. It has been shown that keeping an operating pool of metallurgical cranes during the year will remain with a probability of 0.9985. The proposed approach can be used in current daily activities not only at metallurgical enterprises, but also at others, and also reflected in regulatory and technical documentation.

Keywords

pseudostate, random process, metallurgical crane, Poisson’s law, probability, mathematical expectation, dispersion

For citation

Izvekov Yu.A., Ramazanov K.N., Goncharova E.A., Karimova D.F., Shubin I.G., Smirnova L.V. Quality Management of Operation of Metallurgical Cranes Based on a Method of Pseudostates. Vestnik Magnitogorskogo Gosudarstvennogo Tekhnicheskogo Universiteta im. G.I. Nosova [Vestnik of Nosov Magnitogorsk State Technical University]. 2023, vol. 21, no. 4, pp. 117-123. https://doi.org/10.18503/1995-2732-2023-21-4-117-123

Yury A. Izvekov – DrSc (Eng.), Associate Professor, Head of the Department of Applied Mathematics and Information Science, Nosov Magnitogorsk State Technical University, Magnitogorsk, Russia. Email: This email address is being protected from spambots. You need JavaScript enabled to view it.. ORCID 0000-0002-1892-4055

Kamil N. Ramazanov – DrSc (Eng.), Professor, President of the Academy of Sciences of the Republic of Bashkortostan, Academy of Sciences of the Republic of Bashkortostan, Ufa, Russia. Email: This email address is being protected from spambots. You need JavaScript enabled to view it..

Ekaterina A. Goncharova – student, Nosov Magnitogorsk State Technical University, Magnitogorsk, Russia. Email: This email address is being protected from spambots. You need JavaScript enabled to view it..

Diana F. Karimova – student, Nosov Magnitogorsk State Technical University, Magnitogorsk, Russia. Email: This email address is being protected from spambots. You need JavaScript enabled to view it..

Igor G. Shubin – PhD (Eng.), Associate Professor, Department of Materials Processing Technologies, Nosov Magnitogorsk State Technical University, Magnitogorsk, Russia. Email: This email address is being protected from spambots. You need JavaScript enabled to view it..

Larisa V. Smirnova – PhD (Physics and Mathematics), Associate Professor of the Department of Applied Mathematics and Information Science, Nosov Magnitogorsk State Technical University, Magnitogorsk, Russia. Email: This email address is being protected from spambots. You need JavaScript enabled to view it.. ORCID 0000-0001-6850-6882

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