A Data-Driven Artificial Intelligence Framework for Predictive Maintenance in Smart Manufacturing
DOI:
https://doi.org/10.66280/ijair.v1i1.1Keywords:
Predictive Maintenance Artificial Intelligence Smart Manufacturing Data-Driven Modeling Industrial AIAbstract
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.
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This article is published under the Creative Commons Attribution 4.0 International License (CC BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
