Probabilistic Model of Industrial Motor Reliability as a Function of Lubricant Degradation: A Case Study MDU-01 Motor Hyundai H21/32

This work applies a probabilistic reliability model for industrial diesel engines based on lubricant degradation to the MDU-06 Hyundai H21/32 engine at thermal power station, Ecuador. The engine operated seamlessly from August 2023 until October 2025, generating power continuously. Thus, the model predicts dependability based on oil condition behavior rather than mechanical faults. The study offers a non-linear and multivariate degradation model to track the associated evolution of TBN, Sulfation, Nickel, and Vanadium. Stochastic degradation modeling and Weibull reliability estimation were used to estimate lubricant life under real operating conditions. Results show a Weibull shape parameter β = 8.3 and scale parameter η = 1920 hours, with dependability drastically decreasing after 2000 operational hours. These results demonstrate that lubricant degradation can accurately forecast engine health, allowing predictive maintenance without interruption. Condition-based maintenance (CBM) procedures are optimized to maintain system dependability over 80% while lowering premature wear and maintenance costs with the suggested framework.