Mario De Oliveira

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

Results indicate that combining adaptive signal decomposition with parsimonious dynamical modelling enables effective data synthesis at 10 dB SNR for the tested rotor systems, offering an interpretable alternative to black-box generative models in similar applications.