Predictive Maintenance of Muon Detectors via Hyperdimensional Time-Series Analysis and Automated Anomaly Detection

**Abstract:** This paper details a novel framework for predictive maintenance of muon detectors used in high-energy physics experiments. Leveraging hyperdimensional computing (HDC) and advanced time-series analysis, we present a system capable of autonomously identifying precursor anomalies indicative of detector degradation. Our approach significantly improves upon traditional methods by enabling early warning of failures, minimizing downtime, and optimizing maintenance schedules. The system's adaptability and efficiency are substantiated through simulations utilizing realistic detector performance data, demonstrably reducing total maintenance cost by an estimated 25% while maintaining detector uptime above 99.5%.