System analysis of reliability of electronic dump truck management systems
Abstract
The article provides a systematic analysis of the reliability of electronic control systems for mining dump trucks. Modern approaches to reliability improvement are considered, including a combination of classical methods (analysis of types and consequences of failures, fault tree) and innovative technologies (artificial intelligence, IoT monitoring, digital twins). It is shown that the integration of these methods makes it possible to predict and prevent failures, reducing equipment downtime and repair costs. Special attention is paid to the factors affecting the reliability of operation. The paper also outlines promising areas of development, such as the introduction of autonomous AIbased systems to ensure the safety, economic efficiency and sustainability of mining enterprises.
About the Authors
R. N. Safiullin
St. Petersburg Mining University of Empress Catherine II
Russian Federation
Russian Federation
Doctor of Technical Sciences, Professor, Professor of the Department of Transport and Technological Processes and Machines
St. Petersburg
A. E. Pepler
St. Petersburg Mining University of Empress Catherine II
Russian Federation
Russian Federation
Postgraduate Student
St. Petersburg
P. S. Kuznetsov
St. Petersburg Mining University of Empress Catherine II
Russian Federation
Russian Federation
Student
St. Petersburg
References
1. Земенкова М.Ю., Чижевская Е.Л., Земенков Ю.Д. Интеллектуальный мониторинг состояний объектов трубопроводного транспорта углеводородов с применением нейросетевых технологий // Записки Горного института. 2022. Т. 258 . С. 933-944. DOI: 10.31897/PMI.2022.105
2. Simonova L.A., Dem’yanov D.N., Kapitonov A.A. Smart information system for generating design constraints in the auto industry. Russian Engineering Research. 2020;40(12):1034–1038. https://doi.org/10.3103/S1068798X20120199
3. Беликова Д.Д., Морозов Е.В., Хисамутдинова Э.Л. Оптимальное управление силовыми агрегатами горных машин в диапазоне эксплуатационных режимов при применении системы контроля качества моторного масла. Горный информационно-аналитический бюллетень. 2021;(6):95–103. https://doi.org/10.25018/0236_1493_2021_6_0_95
4. Курганов В.М., Грязнов М.В., Колобанов С.В. Оценка надежности функционирования экскаваторно-автомобильных комплексов в карьере. Записки Горного института. 2020;241:10–21. https://doi.org/10.31897/pmi.2020.1.10
5. Макарова И.В., Ганиев М.М., Баринов А.С., Габсалихова Л.М., Мавляутдинова Г.Р. Современный автотранспорт горнопромышленного комплекса: проблемы и решения. Горная промышленность. 2025;(1S):28– 33. https://doi.org/10.30686/1609-9192-2025-1S-28-33
6. В России зарегистрировано 90 тысяч электрокаров и гибридов [Электронный ресурс] // Автостат: сайт. – URL: https://www.autostat.ru/infographics/58435/ (дата обращения: 15.04.2025)
7. Авксентьев С.Ю., Белоусов В.И. Определение рациональных параметров для безостановочной работы гидротранспортных систем в условиях низких температур. Горная промышленность. 2025;(1S):86– 91. https://doi.org/10.30686/1609-9192-2025-1S-86-91
8. Sadegh Kouhestani, H., Yi, X., Qi, G., Liu, X., Wang, R., Gao, Y., Yu, X., & Liu, L. (2022). Prognosis and Health Management (PHM) of Solid-State Batteries: Perspectives, Challenges, and Opportunities. Energies, 15(18), 6599. https://doi.org/10.3390/en15186599
9. Zhao, J., Zhu, Y., Zhang, B., Liu, M., Wang, J., Liu, C., & Hao, X. (2023). Review of State Estimation and Remaining Useful Life Prediction Methods for Lithium–Ion Batteries. Sustainability, 15(6), 5014. https://doi.org/10.3390/su15065014
10. Akbar, K., Zou, Y., Awais, Q., Baig, M. J. A., & Jamil, M. (2022). A Machine Learning-Based Robust State of Health (SOH) Prediction Model for Electric Vehicle Batteries. Electronics, 11(8), 1216. https://doi.org/10.3390/electronics11081216
11. Huang, S.-C., Tseng, K.-H., Liang, J.-W., Chang, C.-L., & Pecht, M. G. (2017). An Online SOC and SOH Estimation Model for Lithium-Ion Batteries. Energies, 10(4), 512. https://doi.org/10.3390/en10040512
12. X. Cui and T. Hu, "State of Health Diagnosis and Remaining Useful Life Prediction for Lithium-ion Battery Based on Data Model Fusion Method," in IEEE Access, vol. 8, pp. 207298-207307, 2020, https://doi.org/10.1109/ACCESS.2020.3038182
13. Ming-Feng Ge, Yiben Liu, Xingxing Jiang, Jie Liu, A review on state of health estimations and remaining useful life prognostics of lithium-ion batteries, Measurement, 174, 2021, 109057, ISSN 0263-2241, https://doi.org/10.1016/j.measurement.2021.109057
14. Kocsis Szürke, S., Sütheö, G., Apagyi, A., Lakatos, I., & Fischer, S. (2022). Cell Fault Identification and Localization Procedure for Lithium-Ion Battery System of Electric Vehicles Based on Real Measurement Data. Algorithms, 15(12), 467. https://doi.org/10.3390/a15120467
15. Natthida Sukkam, Thossaporn Onsree, Nakorn Tippayawong; Overview of machine learning applications to battery thermal management systems in electric vehicles. AIP Conf. Proc. 17 November 2022; 2681 (1): 020004. https://doi.org/10.1063/5.0115829
Review
For citations:
Safiullin R.N., Pepler A.E., Kuznetsov P.S. System analysis of reliability of electronic dump truck management systems. Social-economic and technical systems: research, design and optimization. 2025;(2):71-77. (In Russ.)
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