In recent decades, Guided waves (GW) have emerged as the most promising modality for Structural Health Monitoring (SHM). In SHM applications with distributed sensor networks, GWs can be efficiently actuated with the help of surface-bonded or embedded piezoelectric elements. Nevertheless, the analysis of GW signals and accurate identification of damage sites still remains a challenge. Algorithms for locating defects, such as Probability Diagnostic Imaging, usually assume unimodal GW propagation. However, in many cases selective mode excitation could be hardly accomplished in practice. From this perspective, decomposition of the measured signal into its constituent components is a critical requirement for accurate damage detection. This work presents a signal processing method that combines time-frequency representation (TFR) with Matching Pursuit (MP) for decoupling of GW modes. The proposed TFR is designed on the basis of the reassigned spectrogram whose kernel is substituted with a Chirp-Z transform in order to improve the resolution without increasing its computational complexity. The MP dictionary is constructed of atoms, which numerically simulate the propagation of wave packets corresponding to different GW modes in the sample. The dictionary also accounts for the effect of bonding between the piezoelectric element and the structure. Performance of the algorithm is demonstrated on aluminum plates and woven composite samples for cases when two fundamental modes S0 and A0 are simultaneously actuated.
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