Machine Learning–Driven Predictive Maintenance for Smart Beverage Manufacturing Systems
The Random Forest model was the best performer, achieving an accuracy of 0.990, precision of 0.918, recall of 0.722, F1-score of 0.808, and ROC-AUC of 0.995 at the optimized threshold of 0.45, demonstrating a practical trade-off between reducing missed breakdown warnings and avoiding excessive unnecessary inspections.