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

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.