An automatic machine fault identification method using the knowledge graph–embedded large language model

Abstract Computer numerical control (CNC) machines are prone to various faults during long-term operation, which can compromise production safety. However, accurately identifying fault sources and obtaining rapid troubleshooting solutions remains a significant challenge. To tackle this challenge, this study proposes an automated machining process decision-making system that integrates a knowledge graph and a large language model (LLM). The system first constructs a graph-based fault knowledge representation using BERT-Transformer-CRF for machining knowledge extraction. The developed machining process knowledge graph is then enhanced and endowed with knowledge reasoning capabilities via the large language model. By combining the graph-structured machining process representing form and knowledge inference ability, the proposed system significantly improves fault diagnosis and troubleshooting efficiency. To evaluate its performance, a functional test was conducted, comparing the system with conventional approaches in terms of accuracy, knowledge inference ability, and user-friendliness. Experimental results in practical industrial scenarios demonstrate that the proposed model achieves 97.50% accuracy in fault diagnosis and troubleshooting. Additionally, subjective evaluations indicate high usability, with scores of 9.4 for user-friendliness and 9.1 for knowledge inference ability. These findings highlight the system’s potential to enhance fault troubleshooting in industrial machining processes.