A Data-Driven Artificial Intelligence Framework for Predictive Maintenance in Smart Manufacturing

Predictive maintenance (PdM) is a cornerstone capability for smart manufacturing, where maintenance actions are increasingly triggered by data and optimized against operational con- straints rather than by fixed schedules. Despite rapid progress in sensing, industrial connectivity, and learning algorithms, practitioners still face persistent gaps: (i) heterogeneous and imperfect data streams; (ii) distribution shift across assets, sites, and operating regimes; (iii) uncertainty in remaining useful life (RUL) estimation and failure risk forecasting; and (iv) the translation from model outputs to actionable maintenance decisions under cost, safety, and availability requirements. This paper proposes a data-driven artificial intelligence framework that unifies (1) an indus- trial data layer for acquisition, synchronization, and feature governance; (2) a modeling layer that supports both sequence-to-RUL regression and time-to-event (survival) prediction with calibrated uncertainty; and (3) a decision layer that maps forecasts to maintenance policies through cost-aware optimization. We provide core mathematical formulations, pseudocode for the training and deployment pipeline, and implementation-ready design choices for edge–cloud execution in modern smart factories. The manuscript is written to be reusable as an engineer- ing reference: assumptions are stated explicitly, interfaces between modules are defined, and evaluation protocols are aligned with common public benchmarks and industrial constraints.