Digital Twin-Based Predictive Maintenance in 6G Smart Stadiums Using Standardized AI-Driven Models

Abstract

The future of smart stadiums lies in highly connected, intricate systems that must respond instantly and maintain smooth operations. When it comes to handling the operational complexity of such dynamic infrastructures, traditional maintenance strategies — whether time-based or reactive — are rapidly falling short. A game-changing method for predictive maintenance is now available through the combination of Digital Twin (DT) technology, artificial intelligence (AI), and 6G connectivity. This research presents the Standardized Predictive AI-Driven Digital Twin Architecture for 6G Smart Stadiums (SPADTA-6G), introducing a new framework. Our goal is to provide a solution that can scale, operate in real-time, and communicate with other systems by leveraging 6G capabilities and standardized AI models for intelligent failure prediction and maintenance scheduling. SPADTA-6G integrates a federated AI learning model, semantic middleware for standardised integration, and a 6G-enabled telemetry layer for ultra-low-latency communication into a multi-layered DT ecosystem. By using attention mechanisms and transformer-based RUL estimation techniques, it enables real-time data interchange and prediction execution. To ensure data governance and interoperability, the framework adheres to developing standards, including the IEEE P7000 series, ISO/IEC TR 24030, and OPC UA. In a virtual stadium setting, the findings indicate that there is less unscheduled equipment downtime, improved prediction accuracy with a low mean RUL error, enhanced latency with reaction times of sub-10 ms using 6G URLLC and mobile edge computing (MEC), and increased scheduling efficiency in maintenance operations. SPADTA-6G provides a foundation for predictive maintenance in 6G-enabled smart venues, ensuring performance, robustness, and compliance with standards. It paves the way for the universal adoption of auto-maintenance systems for smart city infrastructures based on digital twins. This paper presents SPADTA-6G, a standardized predictive AI-driven digital twin architecture for 6G smart stadiums, which achieves up to a 32% reduction in edge energy consumption, a 27%improvement in predictive maintenance accuracy, and a 40% lower false positive rate compared to baseline models. Empirical measurements across heterogeneous stadium assets indicate that median inference latency remains at 7-9 ms, with a 95th-percentile latency of under 10 ms, even under peak operational loads.