AI-Based Predictive Maintenance Framework for Smart Manufacturing Systems

Abstract

Smart manufacturing systems increasingly depend on connected machines, industrial Internet of Things (IIoT) sensors, and data-driven decision support. Unplanned equipment failure can interrupt production, reduce asset availability, and increase maintenance cost. This paper proposes an artificial-intelligence-based predictive maintenance (AI-PdM) framework that integrates machine-condition data acquisition, preprocessing, feature engineering, failure prediction, remaining-useful-life-oriented risk assessment, explainable decision support, and maintenance feedback. The framework is designed for deployment in manufacturing environments where failure observations are rare and heterogeneous sensor variables must be interpreted together. The methodology uses the AI4I 2020 Predictive Maintenance Dataset as a reproducible benchmark because it contains 10,000 records of manufacturing-process conditions and machine-failure labels. A layered evaluation protocol is proposed in which classification performance is assessed by precision, recall, F1-score, area under the receiver-operating characteristic curve, and false-negative rate rather than by accuracy alone. The framework further introduces an explainability layer so that predicted failures can be linked to operational variables such as temperature, rotational speed, torque, and tool wear. The methodological analysis indicates that ensemble learners and deep/hybrid approaches are appropriate for nonlinear failure patterns, while explainability and feedback are essential for industrial acceptance. Published evidence on the same AI4I benchmark reports that tree-based ensemble methods can exceed 90% accuracy, supporting their role as strong baseline models. The resulting framework provides a structured path from raw shop-floor data to maintenance action while emphasizing imbalance-aware evaluation, transparency, and continuous model updating.