Model-Assisted Compressed Sensing for Vibration-Based Structural Health Monitoring

The main challenge in the implementation of long-lasting vibration monitoring systems is to tackle constantly evolving complexity modern “mesoscale” structures. Thus, design energy-aware solutions promoted for joint optimization data sampling rates, onboard storage requirements, and communication payloads. In this context, present work explores feasibility rakeness-based compressed sensing (Rak-CS) approach tune mechanism on second-order statistics measured data. particular, a novel model-assisted variant (MRak-CS) proposed, which built synthetic derivation spectral profile structure by pivoting numerical priors. Moreover, signal-adapted sparsity basis relying wavelet packet transform operator conceived, aims at maximizing signal while allowing precise time-frequency localization. adopted were tested with experiments performed sensorized pinned-pinned steel beam. Results prove that compression strategies are superior conventional eigenvalue approaches standard CS methods. achieved ratio equal seven quality reconstructed structural parameters preserved even presence defective configurations.