AI-based automated system design for efficiency evaluation of performance of mining machines

The article puts forward an innovative method of designing an automated system of mining machine efficiency evaluation based on artificial intelligence. As against conventional methods limited by static indicators and one-dimensional analysis, the proposed system integrates the factors of technology, operation and safety, and unites the quantitative data (fuel consumption, cycle time) with the qualitative linguistic parameters (low/medium/high efficiency). The key difference is the use of the black box model, which simplifies the analysis by focusing on input and output parameters, and fuzzy logic, which ensures interpretability of results through expert rules. This enables the system to adapt fluently to variable operating conditions, and to process incomplete or noisy data in real time. The novelty of the approach is the synthesis of sensor data, GPS and historical sources using the Mamdani algorithm, which ensures an integral estimate including both safety and productivity. The system overcomes constraints of existing solutions which often neglect contextual factors (for instance, variability of downtime) and prove unable to handle linguistic uncertainty. The modular architecture allows calibrating the model on the basis of historical sources and optimizing the rules using machine learning, which improves the model accuracy in the long term. The relevance of the research is conditioned by the growing demand of the mining industry for resource-saving technologies capable of reducing operational costs and minimizing ecological impacts. The proposed method opens up new opportunities for decision-making automation, logistic optimization and risk prediction, and offers potential for introduction in the conditions of digital transformation of mining practices. 

Safiullin Р. N., Parra Z. A., Safiullin R. R., Prisyazhnyuk M. S., Simonova L. A. AI-based automated system design for efficiency evaluation of performance of mining machines. MIAB. Mining Inf. Anal. Bull. 2026;(3):115-135. [In Russ]. DOI: 10.25018/0236_1493_2026_3_0_115.

Р.N. Safiullin¹, Dr. Sci. (Eng.), Professor, e-mail: safravi@mail.ru, ORCID ID: 0000-0002-8765-6461,
Zunilda Arias Parra¹, Graduate Student, e-mail: zuny1503@gmail.com, ORCID ID: 0000-0003-1715-7998,
R.R. Safiullin¹, Cand. Sci. (Eng.), Assistant Professor, e-mail: safiyllin@yandex.ru, ORCID ID: 0000-0003-2315-3678,
M.S. Prisyazhnyuk, Chairman of the Leningrad Region Transport Committee, Saint-Petersburg, Russia, e-mail: transportlo@lenreg.ru,
L.A. Simonova, Dr. Sci. (Eng.), Professor, Corresponding Member of the Russian Academy of Natural Sciences, Naberezhnye Chelny (Institute) Branch of Kazan Federal University, Naberezhnye Chelny, Russia, e-mail: lasimonova@mail.ru, ORCID ID: 0000-0002-3653-1845,
¹ Empress Catherine II Saint-Petersburg Mining University, 199106, Saint-Petersburg, Russia.

Z.A. Parra, e-mail: zuny1503@gmail.com.

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