Predictive maintenance as a tool for the sustainable development of mining enterprises

Abstract

Introduction. To improve the operational stability of mining enterprises, this study examines the relationship between equipment condition, production continuity, energy consumption, and environmental performance. The research focuses on the use of predictive maintenance for mining machinery whose failures directly affect extraction, haulage, crushing, and material transport processes. It is shown that conventional maintenance strategies based only on fixed schedules or emergency repairs do not fully reflect the actual degradation of machines and may fail to prevent unplanned downtime. Methods. The study used operational and diagnostic data obtained from mining dump trucks, hydraulic excavators, drilling rigs, crushing equipment, and belt conveyors. The analyzed parameters included vibration, bearing temperature, hydraulic pressure, fuel consumption, drive power, operating hours, downtime duration, idle time, working cycle duration, and actual production output. The data were processed using normalization, moving averages, trend analysis, reliability indicators, and classification models aimed at detecting pre-failure states and estimating the probability of equipment failure. Results. The findings showed that predictive maintenance improved the technical availability of dump trucks to 0.86–0.91 and reduced unplanned downtime by 28–37%. For crushing equipment, a stable increase in vibration from 3.1–3.4 to 5.8–6.2 mm/s was detected 120–170 h before failure. In hydraulic excavators, degradation was associated with an increase in hydraulic fluid temperature from 58–72 to 82–90 °C and a rise in working cycle duration from 31–34 to 37–41 s. The predictive model achieved 87–92% accuracy in detecting pre-failure states, while fuel and energy indicators confirmed that equipment degradation increases the specific resource intensity of mining operations. Conclusions. It is proposed that predictive maintenance should be considered not only as a diagnostic tool, but also as an element of sustainable management in mining enterprises. The results demonstrate that early detection of equipment degradation can improve reliability, reduce unplanned downtime, stabilize production cycles, lower specific fuel and energy consumption, and decrease the environmental impact of mining operations.