AI-Based Digital Twin for Predictive Maintenance

This paper provides a comprehensive review of the convergence of artificial intelligence (AI) and digital twin (DT) technologies in advanced predictive maintenance (PdM) in modern industrial ecosystems. Traditional maintenance strategies (from reactive maintenance to planned preventive maintenance) often do not account for random degradation patterns, resulting in unplanned equipment downtime or unnecessary operating expenses. By combining real-time Internet of Things (IoT) multi-sensor telemetry streams with high-fidelity physical information and data-driven virtual replicas, AI-powered digital twins provide continuous health monitoring, immediate anomaly isolation and accurate Remaining Useful Life (RUL) calculation. This paper systematically reviews key architectural frameworks, machine learning and deep learning paradigms, edge-cloud deployment topologies, prominent industrial challenges, and future research avenues in Industry 4.0.