Named Entity Recognition in Mechanical Fault Diagnosis: A Template-Free Prompt Learning Approach

Intelligent prediction, accurate diagnosis, and efficient repair of mechanical equipment faults are critical for ensuring production safety and enhancing efficiency in industrial processes. However, data scarcity and recognition efficiency remain significant challenges in named entity recognition (NER) for mechanical equipment faults. To address these issues, this study proposes a novel NER method based on template-free prompt learning. The model is initialized with limited labeled data and then leverages hidden entity cues in unlabeled data to generate insightful hints, guiding the model through deep retraining. Experimental results demonstrate significant improvements in F1 scores: 29.54%, 22.34%, and 19.67% on the MEFD, CONLL03, and MIT-Movie datasets, respectively, as labeled samples increased from 5 shots to 50 shots. Furthermore, compared to template-based prompt learning methods, the proposed approach achieved F1 score improvements of 9.89%, 12.97%, 9.51%, and 2.21% as labeled samples scaled from 5 shots to 50 shots. The proposed method effectively mitigates data dependency issues, enhances model generalization and application capabilities, and provides robust technical support for the intelligent identification and diagnosis of mechanical equipment faults.