The field of industrial maintenance and monitoring is moving towards more intelligent and autonomous systems. Researchers are exploring the use of large language models, collaborative frameworks, and hybrid reasoning approaches to improve the accuracy and efficiency of maintenance tasks. These approaches aim to address the challenges of limited domain adaptability, insufficient real-time performance, and high integration complexity. Notably, some papers have proposed innovative solutions for fault diagnosis, condition monitoring, and predictive control in wastewater treatment plants and other industrial settings.
Some particularly noteworthy papers include: The paper on Towards Next-Generation Intelligent Maintenance, which proposes a collaborative fusion of large and small models for enhancing industrial maintenance. The paper on Hybrid Reasoning for Perception, Explanation, and Autonomous Action in Manufacturing, which introduces a vision-language-action model framework for industrial control. The paper on Feature Engineering for Agents, which proposes a cognitive architecture for ML monitoring that applies feature engineering principles to agents based on Large Language Models.