Work

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

Year: 2025

Type: article

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.

Source: Journal of Dynamics Monitoring and Diagnostics

DOI: 10.37965/jdmd.2025.832

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

Institutions: Xi'an Jiaotong University, Tsinghua University, University of Huddersfield

Language: English

Topic: Machine Fault Diagnosis Techniques

Subfield: Control and Systems Engineering

Field: Engineering

Domain: Physical Sciences

SDG: Industry, innovation and infrastructure

Open Access status: diamond

OpenAlex ID: W4411475608

FWCI: 8.044

Cites:

Cited by: 21

Related to: 10