Industrial Predictive Maintenance Using AI and IoT

Abstract

The Industrial Internet of Things (IIoT) has become a fundamental enabler of smart manufacturing by supporting automation, continuous monitoring, and data-driven operational control in modern industrial systems. Among the various IIoT applications, predictive maintenance plays a crucial role by identifying potential equipment failures in advance, thereby reducing unexpected breakdowns, lowering maintenance expenses, and improving system availability. Despite its benefits, implementing reliable predictive maintenance in IIoT environments is challenging due to dynamic operating conditions, device heterogeneity, high- volume streaming data, and complex fault behaviors. To address these challenges, this paper presents an intelligent ensemble-based predictive maintenance framework that integrates Deep Reinforcement Learning (DRL), Random Forest (RF), and Gradient Boosting Machine (GBM) techniques. The DRL module enables adaptive decision-making by learning optimal maintenance strategies directly from real-time sensor observations. Random Forest is employed to ensure stable and reliable fault classification, particularly in industrial datasets where failure instances are scarce and highly imbalanced. Gradient Boosting Machine further enhances prediction capability by capturing nonlinear feature relationships and identifying rare but critical fault patterns. The proposed framework continuously adapts to variations in operational states and network conditions, allowing proactive maintenance planning and effective fault mitigation. Extensive simulation experiments are carried out using synthetically generated IIoT datasets to evaluate performance in terms of accuracy, precision, recall, F1-score, latency, and fault detection robustness. The results confirm that the proposed ensemble approach outperforms conventional predictive maintenance methods by significantly reducing false alarms, improving fault identification accuracy, and strengthening overall system reliability. This work provides a scalable and intelligent solution suitable for next-generation industrial IoT applications