Guided wave (GW) dispersion curves can be extracted from a time-transient measurement by means of time-frequency representations (TFRs). Unfortunately, any TFR is subject to the time-frequency uncertainty principle. This, in general, limits the capability of TFRs to characterize closely spaced guided modes over a wide frequency range. To overcome this limitation, we implemented a new warped frequency transform that presents enhanced mode extraction capabilities because of a more flexible tiling of the time-frequency domain. The tiling is designed to match the dispersive spectro-temporal structure of a GW by selecting an appropriate map of the time-frequency plane. The proposed transformation is fast, invertible, and covariant to group delay shifts. An application to Lamb waves propagating in an aluminum plate is presented. Time-transient GWs propagation events obtained both numerically and experimentally are considered. The results show that the proposed warped frequency transform limits the interference patterns which appear with other TFRs and produces a sparse representation of the Lamb wave pattern that can be suitable for identification and characterization purposes.

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