Real-Time XR Maintenance Support Integrating Large Language Models in the Era of the Industrial Metaverse

Recent advancements in Artificial Intelligence and eXtended Reality (XR) have laid solid foundations for the development of a new paradigm in industrial maintenance under the light of Industry 5.0 framework. This research presents the design, development, and implementation of an XR-enabled remote maintenance framework that integrates real-time video collaboration, AI-assisted guidance, and a persistent digital asset knowledge layer based on Asset Administration Shells for Maintenance and Repair Operations (MRO). By combining fine-tuned Large Language Models (LLMs) with immersive XR interfaces, the proposed framework enables technicians to interact with virtual representations of industrial assets, access contextual instructions, and receive expert support remotely in real-time. Through seamless integration of historical MRO data, digital twins, and real-time sensor streams, the system facilitates dynamic fault diagnostics and Remaining Useful Life (RUL) estimation. Therefore, the proposed approach is positioned as a Metaverse-aligned implementation, combining synchronous multi-user collaboration, digital–physical coupling through digital twins, and semantic interoperability. The framework is validated through two industrial case studies, demonstrating its feasibility and practical impact on maintenance efficiency and knowledge transfer. The findings position the Industrial Metaverse as a transformative enabler in the future of AI-driven machinery health monitoring.