Optimized predictive maintenance for streaming data in industrial IoT networks using deep reinforcement learning and ensemble techniques

Predictive maintenance in Industrial IoT (IIoT) networks faces challenges due to dynamic conditions, device heterogeneity, and evolving data patterns. This paper introduces an ensemble-based framework combining Deep Reinforcement Learning (DRL), Random Forest (RF), and Gradient Boosting Machines (GBM) to improve fault prediction and maintenance efficiency. Key contributions include: (i) adaptive fault prediction using DRL, which dynamically learns from real-time sensor data to optimize maintenance decisions; (ii) robust fault classification via RF, addressing class imbalance in IIoT environments; and (iii) enhanced predictive accuracy through GBM, leveraging feature dependencies for better generalization. The integrated approach enables dynamic adaptation to changing data, optimized maintenance scheduling, and reduced unplanned downtime. Extensive simulations, evaluated using accuracy, precision, recall, F1-score, and latency, demonstrate superior performance over traditional methods, minimizing false positives and enhancing fault detection reliability. The proposed solution offers a scalable and adaptive predictive maintenance strategy, improving operational efficiency and reducing costs in IIoT-based industrial systems.