Reinforcement learning formulates sim-to-real feature alignment as a Markov decision process to improve vibration-based bearing fault diagnosis under data scarcity.
Nonlinear dynamic modeling and vibration analysis for early fault evolution of rolling bearings,
1 Pith paper cite this work. Polarity classification is still indexing.
1
Pith paper citing it
fields
cs.LG 1years
2026 1verdicts
UNVERDICTED 1representative citing papers
citing papers explorer
-
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.