THE IMPORTANCE OF OIL ANALYSIS IN MAINTENANCE ENGINEERING: A REVIEW ON MINING EQUIPMENT

Creative Commons License

Gündoğar M., Tekin A., Tekin F., Üregen T. G.

3RD INTERNATIONAL KOSOVO ACADEMIC RESEARCH CONGRESS PROCEEDINGS BOOK, Priştine, Kosova, 1 - 03 Ağustos 2026, cilt.1, sa.1, ss.444-466, (Tam Metin Bildiri)

Özet

The high capital cost of mining equipment, the severity of field conditions and the substantial production
losses caused by unexpected failures render the timing of maintenance interventions a critical issue. Oil
analysis, which reveals both component wear and the prevailing condition of the lubricant from a single
sample, is in this respect among the most effective tools available for predictive maintenance. The
present review compares the principal oil analysis methods elemental analysis, particle counting,
viscosity, FTIR, TAN/TBN, water content and ferrography in terms of their diagnostic potential and
inherent limitations. The field applications of these methods are elaborated specifically for haul trucks,
excavators, draglines, crushers, conveyors, hydraulic systems and gearboxes. In contrast to the existing
literature, this study (i) focuses directly on mining equipment, (ii) integrates classical tribological
methods with machine-learning-based fault detection, remaining useful life (RUL) prediction and
decision support systems, and (iii) presents a holistic approach that converts analytical output directly
into maintenance decisions. The principal outcome of the study is that combining oil analysis with
machine learning and reliability-centred maintenance models constitutes the most powerful predictive
approach available to the mining sector.