Reinforcement learning formulates sim-to-real feature alignment as a Markov decision process to improve vibration-based bearing fault diagnosis under data scarcity.
Digital Twins-based prognostic and health management processes for rotating machinery: a review,
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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.
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Digital Twin-Driven Adaptive Sim-to-Real Alignment via Reinforcement Learning for Vibration-Based Bearing Health Monitoring Under Data Scarcity
Reinforcement learning formulates sim-to-real feature alignment as a Markov decision process to improve vibration-based bearing fault diagnosis under data scarcity.
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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.