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

Authors

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

DOI:

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

Keywords:

context learning; fault diagnostics; LLMs; multimodal learning

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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Published

2025-06-21

How to Cite

Chen, X., Lei, Y., Li, Y., Parkinson, S., Li, X., Liu, J., Lu, F., Wang, H., Wang, Z., Yang, B., Ye, S., & Zhao, Z. (2025). Large Models for Machine Monitoring and Fault Diagnostics: Opportunities, Challenges, and Future Direction. Journal of Dynamics, Monitoring and Diagnostics, 4(2), 76–90. https://doi.org/10.37965/jdmd.2025.832

Issue

Section

Future Direction Paper