ARTIFICIAL INTELLIGENCE-ENABLED PREDICTIVE MAINTENANCE IN INDUSTRIAL INTERNET OF THINGS (IIoT): A FRAMEWORK FOR SMART MANUFACTURING SYSTEMS

Abstract

The convergence of the Industrial Internet of Things (IIoT) and artificial intelligence (AI) is reshaping the maintenance paradigm in modern manufacturing, shifting it from reactive and time-based scheduling toward condition-based, predictive intervention. Unplanned equipment downtime remains one of the largest sources of avoidable cost in process and discrete manufacturing, and conventional preventive maintenance addresses it only crudely, replacing components on fixed calendars regardless of their actual condition. This paper develops an integrated, AI-enabled predictive-maintenance (PdM) framework for smart manufacturing systems built on an IIoT data backbone. Adopting a comparative and analytical secondary-research methodology, the study synthesises peer-reviewed literature, industry white papers, and technology reports published between 2018 and 2026 to construct a layered reference architecture spanning smart sensing, edge computing, cloud infrastructure, a machine-learning engine, predictive analytics, decision support, and maintenance execution. The relative performance of the principal AI techniques — artificial neural networks, support vector machines, random forests, deep learning architectures, reinforcement learning, and explainable AI — is examined against the core PdM tasks of fault detection, diagnosis, and remaining-useful-life (RUL) estimation. Comparative analysis indicates that ensemble and deep-learning models consistently achieve higher diagnostic accuracy, while explainability and edge-deployability emerge as decisive criteria for industrial adoption rather than raw accuracy alone. A cost-benefit synthesis drawn from reported industrial deployments suggests that mature AI-PdM programmes can reduce unplanned downtime by 30–50% and maintenance cost by 20–30% while extending asset life. On the strength of this analysis, the paper proposes the AI-Driven Predictive Maintenance Framework for Smart Manufacturing Systems (AIPMF-SMS), a seven-component architecture comprising a data-collection layer, an edge-intelligence layer, an AI analytics engine, a predictive-decision layer, a maintenance-management layer, a continuous-learning and feedback mechanism, and a cross-cutting cybersecurity and governance layer. The framework explicitly addresses the data-quality, scalability, security, and workforce-skill barriers that have constrained real-world deployment. The paper closes with future research directions — digital twins, generative AI, federated learning, and sustainable manufacturing — and with policy and industrial recommendations for accelerating adoption in emerging-economy manufacturing contexts.