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The Lamb-wave-based damage imaging via beamforming techniques, which can visualize the location of damage in the structure intuitively, is one of the most promising methods in the field of structural health monitoring (SHM). However, transducer array position errors are inevitable in practical application, which may lead to serious degradation in imaging performance. In this study, it is shown that the uncertainty of the steering vectors led by the imprecise position of transducers in an array can be suppressed by the doubly constrained robust Capon beamformer (DCRCB). After the unwanted side lobes are restrained by the DCRCB-based coherence factor (CF) weighting, an effective adaptive beamforming damage imaging method robust to transducer position errors is proposed. The numerical simulation and imaging experiment of damage on an aluminum plate are carried out to verify the effectiveness of the proposed algorithm. The results show that the proposed Lamb wave damage imaging method performs better than the reported beamforming ones in literature in terms of resolution, contrast, and robustness to transducer position errors. [ABSTRACT FROM AUTHOR]

In this study, dispersion curve estimation of a bonded aluminum plate is carried out by proposing a specific signal processing procedure. In this proposed method, the angle beam ultrasonic transducer measurement system in a pitch-catch configuration is used to acquire Lamb wave signals from two adjacent positions. The obtained signals are processed then by using signal processing techniques, including bandpass filters, fast Fourier transform (FFT), and bandpass Gaussian filters. Various transmitted signals with different central frequencies are used to estimate four modes of the utilized bonded specimen for frequencies less than 1 MHz. The dispersion curve results in terms of phase velocity and wavenumbers are compared with theoretical dispersion curves and 2-D FFT. This comparison is carried out by using three different metrics, which shows the maximum mean relative error of 3.46% with low variance. [ABSTRACT FROM AUTHOR]