Large Models for Machine Monitoring and Fault Diagnostics: Opportunities, Challenges and Future Direction
Xuefeng Chen, Yaguo Lei, Yan‐Fu Li, Simon Parkinson, Xiang Li, Jinxin Liu, Fan Lü, Huan Wang, Zisheng Wang, Bin Yang, Shilong Ye, Zhibin Zhao
Abstract
As a critical technology for industrial system reliability and safety, machine monitoring and fault diagnostics has 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.