REAL-TIME ENGINE OIL CONDITION MONITORING FOR MINING MACHINE INTERNAL COMBUSTION ENGINES: MULTI-PARAMETER ANALYSIS FRAMEWORK

Аннотация

Mining equipment internal combustion engines operate under extreme conditions, accelerating engine oil degradation through dust contamination, thermal stress, and wear metal generation. This paper presents a comprehensive oil condition monitoring framework integrating used oil analysis (UOA), inline sensor fusion, and predictive maintenance algorithms. Key parameters include viscosity (±10-15% baseline deviation), iron content (>100 ppm alarms), particle counts (>4μm >30,000/mL), and Membrane Patch Colorimetry (MPC >20 ΔE for varnish). Analysis of operational data reveals 25-60% downtime reduction potential through optimized drain intervals and early failure detection. The framework demonstrates 90%+ Remaining Useful Life (RUL) prediction accuracy using multi-sensor AI/ML fusion, validated against mining haul truck and excavator fleets. Implementation roadmap includes same-day UOA turnaround and real-time viscosity/dielectric monitoring for continuous condition assessment.