A strategy for the localization of acoustic emissions (AE) in plates with dispersion and reverberation is proposed. The procedure exploits signals received in passive mode by sparse conventional piezoelectric transducers and a three-step processing framework. The first step consists in a signal dispersion compensation procedure, which is achieved by means of the warped frequency transform. The second step concerns the estimation of the differences in arrival time (TDOA) of the acoustic emission at the sensors. Complexities related to reflections and plate resonances are overcome via a wavelet decomposition of cross-correlating signals where the mother function is designed by a synthetic warped cross-signal. The magnitude of the wavelet coefficients in the warped distance–frequency domain, in fact, precisely reveals the TDOA of an acoustic emission at two sensors. Finally, in the last step the TDOA data are exploited to locate the acoustic emission source through hyperbolic positioning. The proposed procedure is tested with a passive network of three/four piezo-sensors located symmetrically and asymmetrically with respect to the plate edges. The experimentally estimated AE locations are close to those theoretically predicted by the Cramèr–Rao lower bound.

A method for impact location in plate-like structures is proposed. The approach is based on guided waves dispersion compensation. Procedures based on dispersion compensation are usually applied to active monitoring techniques, as they require the knowledge of the time of impact to effectively compensate the guided waves dispersive behaviour. Unfortunately, this knowledge is not given in passive monitoring techniques. Despite this limit, the proposed dispersion compensation procedure is useful as it removes in the group delay of the acquired signals the dependence on the travelled distance. By cross-correlating the signals related to the same event acquired by different sensors, the difference in travelled distances can be determined and used to locate the wave source via hyperbolic positioning. The results show that the developed tool could pave he way for a new class of procedures to locate impacts in waveguides.

Numerous nondestructive evaluations and structural health monitoring approaches based on guide waves rely on analysis of wave fields recorded through scanning laser Doppler vibrometers (SLDVs) or ultrasonic scanners. The informative content which can be extracted from these inspections is relevant; however, the acquisition process is generally time-consuming, posing a limit in the applicability of such approaches. To reduce the acquisition time, we use a random sampling scheme based on compressive sensing (CS) to minimize the number of points at which the field is measured. The CS reconstruction performance is mostly influenced by the choice of a proper decomposition basis to exploit the sparsity of the acquired signal. Here, different bases have been tested to recover the guided waves wave field acquired on both an aluminum and a composite plate. Experimental results show that the proposed approach allows a reduction of the measurement locations required for accurate signal recovery to less than 34% of the original sampling grid.

Ultrasonic tissue characterization has become an area of intensive research. This procedure generally relies on the analysis of the unprocessed echo signal. Because the ultrasound echo is degraded by the non-ideal system point spread function, a deconvolution step could be employed to provide an estimate of the tissue response that could then be exploited for a more accurate characterization. In medical ultrasound, deconvolution is commonly used to increase diagnostic reliability of ultrasound images by improving their contrast and resolution. Most successful algorithms address deconvolution in a maximum a posteriori estimation framework; this typically leads to the solution of l(2)-norm or (1)-norm constrained optimization problems, depending on the choice of the prior distribution. Although these techniques are sufficient to obtain relevant image visual quality improvements, the obtained reflectivity estimates are, however, not appropriate for classification purposes. In this context, we introduce in this paper a maximum a posteriori deconvolution framework expressly derived to improve tissue characterization. The algorithm overcomes limitations associated with standard techniques by using a nonstandard prior model for the tissue response. We present an evaluation of the algorithm performance using both computer simulations and tissue-mimicking phantoms. These studies reveal increased accuracy in the characterization of media with different properties. A comparison with state-of-the-art Wiener and l(1)-norm deconvolution techniques attests to the superiority of the proposed algorithm.

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In this work a new time-frequency procedure for the extraction of multimodal and dispersive guided waves (GWs) from a recorded time-waveform is presented. The proposed "Warped Frequency Transform" (WFT) is based on a timefrequency domain tiling chosen to match the spectro-temporal structure of the different propagating guided waves by selecting an appropriate warping map which generates non-linearly frequency modulated atoms. The WFT transformation is fast, invertible, covariant to group velocity-delay shifts and, in force of the more flexible tiling, presents enhanced modes extraction capabilities. An application to Lamb Waves propagating in an isotropic plate is presented to show the potential of the proposed procedure.

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