Alejandro Vilcacundo-Chiluisa

Maintenance management of stationary combustion engines in the agricultural sector remains largely manual, increasing the risk of unplanned downtime. This study developed a machine learning-based predictive model to anticipate failures within a 60-day horizon, enabling the transition from reactive to proactive maintenance. Following the CRISP-DM (Cross-Industry Standard Process for Data Mining) framework, a sliding-window feature engineering pipeline was built from 2250 historical records spanning 59 engines. Four ensemble learners (Random Forest, LightGBM, XGBoost, and CatBoost) were then compared under two complementary protocols: a strict 60/40 chronological split simulating deployment, and a stratified leave-engine-group-out cross-validation withholding entire engines from training. Nonparametric testing (DeLong test and engine-level cluster bootstrap) showed that the four learners are statistically equivalent, whereas the feature engineering layer contributes a large, significant discrimination gain (ΔAUC $\approx +0.06$, $p\approx {10}^{-27}$) on engines unseen during training. Random Forest, selected as the final model, achieved an AUC of 0.90 with 84.2% recall under the deployment protocol and 0.96 with 90.7% recall under engine-grouped validation. Temporal extrapolation, rather than cross-engine generalization, appeared to be the primary challenge, indicating that rigorously engineered degradation features, more than the choice of ensemble algorithm, drive predictive performance in agricultural maintenance planning.