Abstract

This paper presents an adaptive distributed deep learning framework designed for real-time predictive maintenance within Industrial Internet of Things (IIoT) environments. The framework addresses the challenges of processing massive, high-velocity data streams generated by industrial sensors. We propose a novel architecture that combines edge computing with cloud-based deep learning, enabling real-time anomaly detection and predictive failure analysis. The framework incorporates an adaptive learning mechanism that dynamically adjusts model parameters based on the evolving characteristics of the data stream, ensuring sustained accuracy and robustness. We evaluate the performance of the proposed framework using a real-world industrial dataset and demonstrate its superiority over existing methods in terms of prediction accuracy, latency, and resource utilization.