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 the complexity of modern 'mesoscale' structures. Thus, the design of energy-aware solutions is promoted for the joint optimization of data sampling rates, on-board storage requirements, and communication data payloads. In this context, the present work explores the feasibility of the model-assisted rakeness-based compressed sensing (MRak-CS) approach to tune the sensing mechanism on the second-order statistics of measured data by pivoting on numerical priors. Moreover, a signal-adapted sparsity basis relying on the Wavelet Packet Transform is conceived, which aims at maximizing the signal sparsity while allowing for a precise time-frequency localization. The adopted solutions were tested with experiments performed on a sensorized pinned-pinned steel beam. Results prove that the proposed compression strategies are superior to conventional eigenvalue approaches and to standard CS methods. The achieved compression ratio is equal to 7 and the quality of the reconstructed structural parameters is preserved even in presence of defective configurations.