Automated Anomaly Detection and Predictive Maintenance in Digital Twin-Enabled Industrial Robotics via HyperScore-Guided Multi-Modal Analysis

**Originality:** This research introduces a novel anomaly detection and predictive maintenance system within digital twin environments for industrial robotics, leveraging HyperScore-guided multi-modal data fusion and a tailored reinforcement learning framework. Unlike traditional methods focusing on single data streams, our approach integrates visual, vibrational, and thermal data, dynamically weighting their importance based on a HyperScore model ensuring prioritized and accurate anomaly identification, thereby drastically minimizing downtime.