Multi-Modal Anomaly Detection & Predictive Maintenance in Rittal Wall-Mounted Enclosures via HyperScore Integration

**Abstract:** This research introduces a novel system for real-time anomaly detection and predictive maintenance within Rittal wall-mounted enclosures, significantly reducing downtime and operational costs in industrial environments. Leveraging multi-modal sensor data (vibration, temperature, humidity, acoustic emissions) alongside enclosure structural metadata, the system employs a multi-layered evaluation pipeline culminating in a "HyperScore" for anomaly prioritization. This scoring system, based on principles of Information Theory and Bayesian Calibration, robustly integrates disparate data streams, minimizing false positives and maximizing predictive accuracy. The system's commercial readiness is facilitated by the reliance on established sensor technologies, modular architecture, and compatibility with existing industrial automation platforms. This paper details the system's architecture, methodology, and validation results demonstrating a 30% reduction in unplanned maintenance events compared to traditional threshold-based monitoring systems.