文献来源：

Large Models for Machine Monitoring and Fault Diagnostics: Opportunities, Challenges, and Future Direction

Xuefeng Chen,¹* Yaguo Lei,¹* Yan-Fu Li,²* Simon Parkinson,³* Xiang Li,¹ Jinxin Liu,¹ Fan Lu,¹ Huan Wang,² Zisheng Wang,² Bin Yang,¹ Shilong Ye,² and Zhibin Zhao¹

¹School of Mechanical Engineering, Xi'an Jiaotong University, Xi'an, China

²Department of Industrial Engineering, Tsinghua University, Beijing, China

³Department of Computer Science, University of Huddersfield, Huddersfield, UK

文章摘要：Abstract

Abstract: As a critical technology for industrial system reliability and safety, machine monitoring and fault diagnostics have advanced transformatively with large language models (LLMs). This paper reviews LLM-based monitoring and diagnostics methodologies, categorizing them into in-context learning, fine-tuning, retrieval-augmented generation, multimodal learning, and time series approaches, analyzing advances in diagnostics and decision support. It identifies bottlenecks like limited industrial data and edge deployment issues, proposing a three-stage roadmap to highlight LLMs’ potential in shaping adaptive, interpretable PHM frameworks.

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