A knowledge-guided two-stage Transformer framework achieves 92.61% average accuracy in cross-domain bearing fault diagnosis using only 10% labeled target data on four real-world datasets, outperforming prior methods by 17.24 points.
The Interpretable Reasoning and Intelligent Decision-Making Based on Event Knowledge Graph With LLMs in Fault Diagnosis Scenarios,
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An LLM-based Two-Stage Transformer Framework for Cross-Domain Bearing Fault Diagnosis with Limited Data
A knowledge-guided two-stage Transformer framework achieves 92.61% average accuracy in cross-domain bearing fault diagnosis using only 10% labeled target data on four real-world datasets, outperforming prior methods by 17.24 points.