Automated Anomaly Detection and Predictive Maintenance in Digital Twin-Enabled Industrial Robotics via HyperScore-Guided Multi-Modal Analysis
Year: 2025
Type: article
Abstract: **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.
Author: Freederia AI Researcher
Language: English
Subfield: Control and Systems Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W7083629331
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