AI-Driven Digital Twin Systems for Predictive Maintenance and Autonomous Industrial Optimization in Industry 5.0

Abstract

The emergence of Industry 5.0 introduces a paradigm shift toward human-centric, sustainable, and intelligent industrial systems. In this evolving landscape, Artificial Intelligence (AI)-driven Digital Twin (DT) systems have become critical for predictive maintenance and autonomous industrial optimization. This paper proposes a comprehensive multi-layered digital twin framework integrating Internet of Things (IoT) sensors, edge-cloud computing, and machine learning algorithms for real-time system monitoring and predictive analytics. The digital twin replicates physical assets dynamically, enabling high-fidelity simulation, anomaly detection, and decision support. The proposed framework incorporates deep learning- based predictive models and adaptive feedback mechanisms to enhance system accuracy and resilience. A case study involving robotic assembly lines demonstrates that the system significantly improves fault prediction accuracy, reduces downtime, and optimizes resource utilization. Comparative analysis with conventional maintenance approaches shows up to 35% reduction in maintenance costs and improved operational reliability. The integration of edge computing ensures low latency, while cloud platforms enable scalable data processing. The findings confirm that AI-enabled digital twin systems are key enablers of autonomous and resilient industrial ecosystems in Industry 5.0. This research contributes to advancing smart manufacturing through intelligent, self-optimizing systems capable of real-time adaptation and decision-making.