Development of neural networks to analyze vibration signals of mining equipment and prevent emergency situations

DOI: https://doi.org/10.30686/1609-9192-2025-2-97-104

Читать на русскоя языкеO.V. Panina, N.A. Zavalko, S.G. Eremin, K.V. Kharchenko, S.A. Zudenkova
Financial University under the Government of the Russian Federation, Moscow, Russian Federation
Russian Mining Industry №2 / 2025 p.97-104

Abstract: The article discusses the application of neural networks to analyze vibration signals of mining equipment in order to predict and prevent emergencies. A multilayer architecture of neural network trained has been developed using data from vibration sensors of real equipment. The proposed method allows to achieve high accuracy (95%) in classifying vibration patterns corresponding to different states of the equipment, i.e. from normal operation mode to pre-emergency states. Based on the analysis of spectral and temporal characteristics of vibrations, the method provides early (30-120 minutes) warning on development of potential faults. Experimental studies on real data confirm the ability of the neural network approach to reduce the number of emergency downtimes by 40% and the cost of emergency repairs by 25%. The proposed method opens prospects for creation of intelligent systems to ensure safety and efficiency of mining equipment based on predictive analytics.

Keywords: vibration diagnostics, neural networks, accident prediction, mining equipment, intelligent data analysis

For citation: Panina O.V., Zavalko N.A., Eremin S.G., Kharchenko K.V., Zudenkova S.A. Development of neural networks to analyze vibration signals of mining equipment and prevent emergency situations. Russian Mining Industry. 2025;(2):97–104. (In Russ.) https://doi.org/10.30686/1609-9192-2025-2-97-104

Article info

Received: 18.01.2025

Revised: 27.02.2025

Accepted: 02.03.2025

Information about the authors

Olga V. Panina – 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.

Natalia A. Zavalko – Dr. Sci. (Econ.), 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.

Sergey G. Eremin – Cand. Sci. (Law), 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.

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.

Svetlana A. Zudenkova – 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.

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