A. De luca

Interpretable physics-guided data augmentation for rotating machinery using empirical wavelet transform and sparse identification of nonlinear dynamics

Data scarcity limits the development of machine-learning-based fault diagnosis systems for rotating machinery, especially under noise and varying operating conditions. This paper presents an interpretable, physics-guided data augmentation framework in which empirical wavelet transform (EWT), time-delay embedding and sp...