Predictive Maintenance Optimization Using Machine Learning for Industrial Equipment Reliability

Abstract

Predictive maintenance models are often evaluated primarily as classifiers, although practical maintenance decisions depend on the relative consequences of preventive interventions and missed failures. This study develops a reliability-oriented predictive maintenance framework that integrates machine-learning failure probabilities with cost-sensitive maintenance thresholds. A reproducible simulation of 80,000 industrial-equipment cycles was constructed from the documented process envelopes and failure mechanisms of the AI4I 2020 benchmark, while the original UCI dataset was used only as a calibration reference. Six raw operating variables were supplemented with temperature difference, mechanical power, and a type-adjusted wear–torque index. Four classifiers—logistic regression, random forest, extra trees, and XGBoost—were evaluated using stratified training, validation, and independent test partitions. Random forest achieved the strongest test PR-AUC of 0.967, ROC-AUC of 0.989, F1 of 0.965, and MCC of 0.964. Incorporating physics-derived variables increased PR-AUC from 0.943 to 0.966 and reduced the Brier score from 0.0088 to 0.0035. Cost-sensitive thresholds reduced normalized maintenance costs by 46.3–88.9% relative to reactive maintenance across failure-to-preventive-maintenance cost ratios of 2:1 to 20:1. Moderate sensor perturbations preserved predictive performance, whereas 10% standard-deviation noise substantially reduced PR-AUC and recall. These findings demonstrate that predictive performance, engineering-informed feature design, sensor robustness, and maintenance economics should be evaluated jointly before industrial deployment.