Enhancing Mining Equipment Reliability and Lubrication Cost Optimization through Oil Analysis-Based Predictive Maintenance

Reliability of mining equipment is crucial to the productivity and economic growth of mining organizations. However, high levels of stress and extreme environmental conditions pose a threat to mining equipment, often leading to high failure rates. The purpose of this study is to investigate the impact of using oil analysis-based predictive maintenance on mining equipment reliability and lubrication costs. A triangulation-mixed approach combining experimental analysis and a structured survey was employed across three (3) mining companies, four (4) mining contractors in Ghana, and seven critical mining equipment items were selected to determine the technical and economic impact of oil analysis. Oil samples from this equipment were analyzed for wear elemental composition (ASTM D6595), viscosity (ASTM D445), particle count (ISO 4406), acid and base numbers (ASTM D664 and D2896), silicon and water contamination (ASTM D6304) using calibrated laboratory analytical instruments to examine their effect on equipment reliability based on the results. Experimental oil analysis results from the sampled critical equipment revealed the Liebherr excavator engine had the highest measured iron concentration at 6 ppm, exceeding the site-specific AngloGold (AGA) standard’s warning limit of 5 ppm. The copper levels within both the Lightning agitator and Hitachi excavator were elevated to 2 ppm and 3 ppm, respectively. Aluminum was elevated to 3 ppm within the SAG Mill gearbox, Silicon contamination exceeding the warning threshold of 2 ppm in the agitator gearbox (3 ppm) and the Liebherr excavator engine (4 ppm), and 2% water contamination in the Metso pebble crusher. The Liebherr excavator and Agitator samples measured 30 ppm and 35 ppm (indicating 45% and 36% depletion of the zinc additive) compared to the warning limit of 50% (27.5 ppm) additive concentration in fresh oil samples. Furthermore, all used oil samples viscosities were analyzed based on industry-acceptable limits of ±10%, 0.05% for water contamination, and 50% critical limit for TBN and TAN. All potential failures were detected, as evidenced by oil analysis data, and timely corrective action was implemented before any functional failure of the mining equipment, enhancing reliability by function, rather than metrics in the study. By implementing oil analysis-based predictive maintenance, the mining companies reduced lubricant costs by 16.6% per year. These findings reveal that oil analysis is technically and economically viable and an optimal approach for predictive maintenance in mining operations.