Alessandro Perelli

Acoustic emission localization in plates with dispersion and reverberations using sparse PZT sensors in passive mode

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 Cram?r?Rao lower bound.

Compressive sensing of full wave field data for structural health monitoring applications

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.

Model-based compressive sensing for damage localization in lamb wave inspection

Compressive sensing (CS) has emerged as a potentially viable technique for the efficient compression and analysis of high-resolution signals that have a sparse representation in a fixed basis. In this work, we have developed a CS approach for ultrasonic signal decomposition suitable to achieve high performance in Lamb-wave-based defect detection procedures. In the proposed approach, a CS algorithm based on an alternating minimization (AM) procedure is adopted to extract the information about both the system impulse response and the reflectivity function. The implemented tool exploits the dispersion compensation properties of the warped frequency transform as a means to generate the sparsifying basis for the signal representation. The effectiveness of the decomposition task is demonstrated on synthetic signals and successfully tested on experimental Lamb waves propagating in an aluminum plate. Compared with available strategies, the proposed approach provides an improvement in the accuracy of wave propagation path length estimation, a fundamental step in defect localization procedures.

Joint Multichannel Blind Speech Separation and Dereverberation: A Real-Time Algorithmic Implementation

Blind source separation (BSS) and dereverberation have been deeply investigated due to their importance in many applications, as in image and audio processing. A two-stage approach leading to a sequential source separation and speech dereverberation algorithm based on blind channel identification (BCI) has recently appeared in literature and taken here as reference. In this contribution, a real-time implementation of the aforementioned approach is presented. The optimum inverse filtering algorithm based on the Bezout’s Theorem and used in the dereverberation stage has been substituted with an iterative technique, which is computationally more efficient and allows the inversion of long impulse responses in real-time applications. The entire framework works in frequency domain and the NU-Tech software platform has been used on purpose for real-time simulations.

Best basis compressive sensing of guided waves in structural health monitoring

A novel signal compression and reconstruction procedure suitable for guided wave based structural health monitoring (SHM) applications is presented. The proposed approach combines the wavelet packet transform and frequency warping to generate a sparse decomposition of the acquired dispersive signal. The sparsity of the signal in the considered representation is exploited to develop data compression strategy based on the Best-Basis Compressive sensing (CS) theory. The proposed data compression strategy has been compared with the transform encoder based on the Embedded Zerotree (EZT), a well known data compression algorithm. These approaches are tested on experimental Lamb wave signals obtained by acquiring acoustic emissions in a 1 m 2 aluminum plate with conventional piezoelectric sensors. The performances of the two methods are analyzed by varying the compression ratio in the range 40-80%, and measuring the discrepancy between the original and the reconstructed signal. Results show the improvement in signal reconstruction with the use of the modified CS framework with respect to transform-encoders such as the EZT algorithm with Huffman coding. Compressive Sensing based on wavelet analysis and frequency warping operator.Frequency warping Wavelet analysis.Compression of Ultrasonic Lamb Waves.Acoustic emission localization in plates with dispersion and reverberation.Procedure is tested with a passive network of three piezo-sensors.

Frequency warped cross-wavelet multiresolution analysis of guided waves for impact localization

Passive source localization in dispersive systems with sparse sensors array represent a fundamental issue in applications such as seismic, radar, underwater acoustics, wireless transmission. This paper presents a new in situ Structural Health Monitoring (SHM) system based on wave propagation approach able to assess damages and to identify the location of acoustic emission (AE) sources due to impacts. When we deal with such channels, it is necessary to compensate the frequency dependent propagation and then the localization is achieved from time-difference-of-arrival (TDOA) between sensor outputs. In this paper a novel impact localization algorithm based on the frequency warping unitary operator applied to E-spline wavelet multiresolution analysis is presented. Unitary frequency warped representation is important to analyze class of signal covariant to group delay shift as those propagating through frequency-dependent channels. The innovative key points behind the developed framework are: (i) to perform a nonstationary wavelet multiresolution analysis on the acquired signals; (ii) to design a proper scaling wavelet through the frequency warping operator; (iii) application of the frequency warped wavelet multiresolution on the cross-correlated signal to achieve an accurate time difference of arrival (TDOA) estimation. Finally, the TDOA data are exploited to locate the acoustic emission source through hyperbolic positioning. The Cramer-Rao lower bound (CRLB) is derived and the performance of the proposed algorithm is analyzed. It is found that the theoretical variances of the estimates are unbiased and approach the CRLB at high signal-to-noise ratio (SNR). In order to demonstrate the effectiveness of the proposed framework, we have investigated lamb wave transmission over aluminum plate that suffers from severe multi modal frequency dispersions and multipath reflections.

Localization of defects in irregular waveguides by dispersion compensation and pulse compression

In this work a pulse-echo procedure suitable to locate defect-induced reflections in irregular waveguides is proposed. In particular, the procedure extracts the distance of propagation of a guided wave scattered from a defect within the echo signal, revealing thus the source-defect distance. To such purpose, first, a Warped Frequency Transform (WFT) is used to compensate the signal from the dispersion of the guided wave due to the traveled distance in a portion of the waveguide that is assumed as reference. Next, a pulse compression procedure is applied to remove the additional dispersion introduced by the remaining irregular portion of the waveguide. Thanks to this processing the actual distance traveled by the wave in the regular portion of the irregular waveguide is revealed. Thus the proposed strategy extends pulse-echo defect localization procedures based on guided waves to irregular waveguides. Since the processing is based on Fast Fourier Transforms, the algorithm can be easily implemented in real time applications for structural health monitoring. The potential of the procedure is numerically demonstrated by processing Lamb waves propagating in an irregular waveguide composed by aluminum plates with different thicknesses and tapered portions.

Compressive computed tomography image reconstruction with denoising message passing algorithms

In this paper we address the compressive reconstruction of images from a limited number of projections in order to reduce the X-ray radiation dose in Computed Tomography (CT) while achieving high diagnostic performances. Our objective is to study the feasibility of applying message passing Compressive Sensing (CS) imaging algorithms to CT image reconstruction extending the algorithm from its theoretical domain of i.i.d. random matrices. Exploiting the intuition described in [1] of employing a generic denoiser in a CS reconstruction algorithm, we propose a denoising-based Turbo CS algorithm (D-Turbo) and we extend the application of the de-noising approximate message passing (D-AMP) algorithm to partial Radon Projection data with a Gaussian approximation of the Poisson noise model. The proposed CS message passing approaches have been tested on simulated CT data using the BM3D denoiser [2] yielding an improvement in the reconstruction quality compared to existing direct and iterative methods. The promising results show the effectiveness of the idea to employ a generic denoiser Turbo CS or message passing algorithm for reduced number of views CT reconstruction.

Design of an ultra-low power device for aircraft structural health monitoring

One of the popular structural health monitoring (SHM) applications of both automotive and aeronautic fields is devoted to the non-destructive localization of impacts in plate-like structures. The aim of this paper is to develop a miniaturized, self-contained and low power device for automated impact detection that can be used in a distributed fashion without central coordination. The proposed device uses an array of four piezoelectric transducers, bonded to the plate, capable to detect the guided waves generated by an impact, to a STM32F4 board equipped with an ARM Cortex-M4 microcontroller and a IEEE802.15.4 wireless transceiver. The waves processing and the localization algorithm are implemented on-board and optimized for speed and power consumption. In particular, the localization of the impact point is obtained by cross-correlating the signals related to the same event acquired by the different sensors in the warped frequency domain. Finally the performance of the whole system is analysed in terms of localization accuracy and power consumption, showing the effectiveness of the proposed implementation.

Compressive Sensing with warped frequency models in lamb waves damage detection procedures

Compressive Sensing (CS) has emerged as a potentially viable technique for the efficient acquisition of high-resolution signals that have a sparse representation in a fixed basis. In this work, we have developed a general approach for low rate sampling and efficient CS impulse response recovery algorithms that exploits convolution signal models of dispersive ultrasonic guided waves with a sparse representation in the frequency warped basis. We apply our framework to both to lower the sampling frequency and to enhance defect localization performances of Lamb wave inspection systems. The reconstruction algorithm is based on both the iterative support estimation and alternating minimization algorithm to further improve localization accuracy, separating the contribution of the exciting wave. As a result, an automatic detection procedure to locate defect-induced reflections was demonstrated and successfully tested on experimental Lamb waves propagating in an aluminum plate.