Reinforcement learning formulates sim-to-real feature alignment as a Markov decision process to improve vibration-based bearing fault diagnosis under data scarcity.
A weighted DJP-MMD based deep transfer metric learning for the fault diagnosis of bearing under variable working conditions,
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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.