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This paper proposes a reinforcement learning-driven adaptive sim-to-real alignment method for vibration-based bearing health monitoring, addressing data scarcity and heterogeneous fault-type gaps via proximal policy optimization.
Proposes a lightweight neural architecture search performed directly on the deployment device for near-sensor computing, validated on sEMG sign language and fault diagnosis datasets, achieving improved accuracy and reduced RAM occupancy.
Proposes RGNet, a neural network architecture based on renormalization group theory for hierarchical coarse-graining of feature space to address class imbalance and noise in fault diagnosis. Experimental results on the AI4I dataset show RGNet provides interpretable and competitive performance.
The paper proposes a reliable fault diagnosis method using a belief rule base with robustness analysis, addressing sensor reliability issues, and validates the approach on WD615 diesel engine and bearing datasets.