Development of neural networks to analyze vibration signals of mining equipment and prevent emergency situations
O.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: opanina@fa.ru
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: nazavalko@fa.ru
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: SGEremin@fa.ru
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: KVKharchenko@fa.ru
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: SAZudenkova@fa.ru
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