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This study introduces an AI-driven Remote Expert System that transforms industrial maintenance through the integration of artificial intelligence (AI), mixed reality (MR), and natural language processing (NLP) technologies. Our implementation utilizes YOLOv8s on Microsoft HoloLens 2 for real-time object recognition and LED status indicator detection on PLCnext controllers, enabling on-site operators to receive immediate diagnostic information. Experimental validation and field trials demonstrate robust system performance with an mAP 50 of 0.993 and mAP 50–95 of 0.844, while optimizing CPU utilization and battery consumption during video streaming operations. The system automatically generates NLP-based maintenance recommendations corresponding to identified issues. This innovative approach establishes a new paradigm for industrial maintenance that significantly reduces equipment downtime, enhances operational efficiency, and minimizes maintenance costs.

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AI-driven Remote Expert System Architecture Approach: An Integration of AI, MR, and NLP

  • 会议论文

  • 发表日期：

    2026年

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  • Bhusana Kongon,
  • Benchaporn Jantarakongkul,
  • Tanat Kanangnanon,
  • Prajaks Jitngernmadan

摘要

This study introduces an AI-driven Remote Expert System that transforms industrial maintenance through the integration of artificial intelligence (AI), mixed reality (MR), and natural language processing (NLP) technologies. Our implementation utilizes YOLOv8s on Microsoft HoloLens 2 for real-time object recognition and LED status indicator detection on PLCnext controllers, enabling on-site operators to receive immediate diagnostic information. Experimental validation and field trials demonstrate robust system performance with an mAP 50 of 0.993 and mAP 50–95 of 0.844, while optimizing CPU utilization and battery consumption during video streaming operations. The system automatically generates NLP-based maintenance recommendations corresponding to identified issues. This innovative approach establishes a new paradigm for industrial maintenance that significantly reduces equipment downtime, enhances operational efficiency, and minimizes maintenance costs.