Analyzing the efficiency of implementing predictive maintenance of mining equipment based on Industry 4.0 technologies

DOI: https://doi.org/10.30686/1609-9192-2024-4-130-138

Читать на русскоя языкеK.V. Kharchenko, A.Zh. Zubets, E.I. Moskvitina, L.K. Babayan, A.M. Laffah
Financial University under the Government of the Russian Federation, Moscow, Russian Federation
Russian Mining Industry №4 / 2024 p.130-138

Abstract: The mining industry plays a key role in the global economy, providing raw materials to various industries. However, the operational efficiency of mining equipment remains a serious issue due to high maintenance costs and downtime caused by its failures. The relevance of the study is defined by the potential of using the Industry 4.0 technologies to improve the efficiency of mining equipment maintenance. The purpose of the work is to evaluate the efficiency of implementing predictive maintenance systems based on the Industry 4.0 technologies and to develop recommendations for their development in the industry. The methodology includes an analysis of the technology adoption level in 2013–2023, collection of the KPI data to assess the impact of predictive maintenance, studying the economic efficiency of investments, the development of models for predicting failures and optimizing maintenance strategies. The results showed a significant increase in the implementation level of the Industry 4.0 technologies, improved KPIs and high economic efficiency of investments in predictive maintenance systems. The developed models demonstrated high accuracy of failure prediction and optimization of the maintenance strategies. Recommendations are formulated for the efficient implementation of predictive maintenance systems with account for the specific features of the industry. The research has theoretical significance for the development of the predictive maintenance concept and practical value for the mining enterprises. Further research may be directed towards the development of the industry standards and the integration of predictive maintenance systems with other management processes.

Keywords: mining industry, predictive maintenance, Industry 4.0, operational efficiency, technical availability, machine learning, big data

For citation: Kharchenko K.V., Zubets A.Zh., Moskvitina E.I., Babayan L.K., Laffah A.M. Analyzing the efficiency of implementing predictive maintenance of mining equipment based on Industry 4.0 technologies. Russian Mining Industry. 2024;(4):130–138. (In Russ.) https://doi.org/10.30686/1609-9192-2024-4-130-138

Article info

Received: 23.05.2024

Revised: 04.07.2024

Accepted: 11.07.2024

Information about the authors

Konstantin V. Kharchenko – Cand. Sci. (Sociol.), Associate Professor of the Department of State and Municipal Administration, Financial University under the Government of the Russian Federation, Moscow, Russian Federation; e-mail: This email address is being protected from spambots. You need JavaScript enabled to view it.

Anton Zh. Zubets – Cand. Sci. (Econ.), Associate Professor of the Department of State and Municipal Administration, Financial University under the Government of the Russian Federation, Moscow, Russian Federation; e-mail: This email address is being protected from spambots. You need JavaScript enabled to view it.

Ekaterina I. Moskvitina – Cand. Sci. (Econ.), Associate Professor of the Department of State and Municipal Administration, Financial University under the Government of the Russian Federation, Moscow, Russian Federation; e-mail: This email address is being protected from spambots. You need JavaScript enabled to view it.

Levon K. Babayan – Assistant of the Department of State and Municipal Administration, Financial University under the Government of the Russian Federation, Moscow, Russian Federation; e-mail: This email address is being protected from spambots. You need JavaScript enabled to view it.

Adam M. Laffah – Assistant of the Department of State and Municipal Administration, Financial University under the Government of the Russian Federation, Moscow, Russian Federation; e-mail: This email address is being protected from spambots. You need JavaScript enabled to view it.

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