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

Xuefeng Chen; Yaguo Lei; Yanfu Li; Simon Parkinson; Xiang Li; Jinxin Liu; Fan Lu; Huan Wang; Zisheng Wang; Bin Yang; Shilong Ye; Zhibin Zhao

doi:10.37965/jdmd.2025.832

Authors

Xuefeng Chen Xi’an Jiaotong University
Yaguo Lei Xi’an Jiaotong University
Yanfu Li Tsinghua University
Simon Parkinson University of Huddersfield
Xiang Li Xi’an Jiaotong University https://orcid.org/0000-0003-0569-2176
Jinxin Liu Xi’an Jiaotong University
Fan Lu Xi’an Jiaotong University
Huan Wang Tsinghua University https://orcid.org/0000-0002-1403-5314
Zisheng Wang Tsinghua University
Bin Yang Xi’an Jiaotong University https://orcid.org/0000-0002-3015-3580
Shilong Ye Tsinghua University
Zhibin Zhao Xi’an Jiaotong University https://orcid.org/0000-0003-4180-7137

DOI:

https://doi.org/10.37965/jdmd.2025.832

Keywords:

LLMs, context learning, multimodal learning, fault diagnostics

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.