An LLM‐Enabled Human–Machine Collaborative Fault Diagnosis Framework via Industrial AI Agents

ABSTRACT Intelligent fault diagnosis (FD) of mechanical equipment is vital for ensuring the safety and stability of industrial systems. As modern industrial environments continue to expand in scale and complexity, systematic FD increasingly requires not only accurate signal pattern recognition but also flexible human–machine collaboration for task configuration, model selection and decision optimisation. Traditional static diagnostic systems based on small‐scale specialised models (SSMs) are often inadequate for dynamic, multisource and heterogeneous diagnostic tasks. To address these challenges, this paper proposes a large language model (LLM)‐enabled human–machine collaborative fault diagnosis framework that combines multimodal task interpretation, signal‐source identification and dynamic selection of registered SSMs. The LLM operates at the task‐cognition level, whereas structured task checking, model applicability validation and human review for uncertain cases prevent uncertain cognitive outputs from directly triggering deterministic diagnosis. Experiments on two public datasets and an industrial spindle‐motor dataset show that the agent achieves 87.33% SSM selection accuracy and 75.00% end‐to‐end diagnosis accuracy without additional labelled samples for training a separate router, outperforming deterministic rule routing. These results indicate a feasible architecture for heterogeneous industrial diagnosis that combines flexible LLM‐based task understanding, deterministic SSM diagnosis and explicit human review for uncertain tasks.