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.
Simulated Data-Assisted Fault Diagnosis Framework With Dual-Path Feature Fusion for Rolling Element Bearings Under Incomplete Data,
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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.