Minimum-Variance Imaging in Plates Using Guided-Wave-Mode Beamforming

Sternini, Simone; Pau, Annamaria; Scalea, Francesco Lanza di

doi:10.1109/tuffc.2019.2935139

Cited by 19 publications

(13 citation statements)

References 44 publications

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“…The optimization function min boldwx,yHboldRx,yboldwx,y minimizes the imaging index at all imaging points, but the constraint of the term of boldwx,yHe=1 preserves the imaging index at the location where the vector boldrx,y is proportional to the look direction. The look direction is sometimes referred to as the “replica vector.” 23 The elements in the vector boldrx,y are expected to be the same values if there is a scatterer in the point (x,y), so the look direction bolde is defined to be a two-norm normalized vector proportional to…”

Section: Methodsmentioning

confidence: 99%

“…The look direction is sometimes referred to as the "replica vector." 23 The elements in the vector r x,y are expected to be the same values if there is a scatterer in the point ðx,yÞ, so the look direction e is defined to be a two-norm normalized vector proportional to e : ½1; 1, …, 1� T (19) Equation ( 18) can be solved by the use of a Lagrange multiplier λ, namely…”

Section: Waveform Covariancementioning

confidence: 99%

“…22 Sternini et al proposed a modified MVDR imaging algorithm using the updated look directions for Lamb wave phased array. 23 Yang et al proposed a frequency response function–based TFM algorithm considering dispersion. 24 For phase-based imaging, the sign coherence factor (SCF) 25 is one of the representatives.…”

Section: Introductionmentioning

confidence: 99%

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Waveform covariance imaging for Lamb wave phased array

Deng

2022

Structural Health Monitoring

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The amplitude and the phase information are both important for damage localization in Lamb wave–based structural health monitoring and non-destructive testing. Most previous studies in Lamb wave imaging are only based on either the amplitude or phase of the signal at the time of flight. In this study, a post-processing technique for Lamb wave phased array imaging is proposed for isotropic plates, which simultaneously utilizes both the amplitude and phase information within a small neighborhood centered at the time of flight. In the proposed imaging algorithm, a modified virtual time reversal is firstly implemented to compensate for dispersion and the amplitude decrease caused by wave diffusion. Then the waveform covariance between any two processed wave packets, which contains information of both amplitude and phase, is used as the indicator of the presence of damage. Finally, adaptive weights based on minimum variance are introduced to weight and sum the waveform covariance for damage imaging. Based on a uniform circular array, experimental results on an aluminum plate with three defects verify that the proposed algorithm is capable of localizing multiple defects, suppressing background noise and main lobe width.

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Section: Methodsmentioning

confidence: 99%

Section: Waveform Covariancementioning

confidence: 99%

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Waveform covariance imaging for Lamb wave phased array

Deng

2022

Structural Health Monitoring

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“…[1][2][3][4][5][6][7] Of particular interest are damage detection techniques that can be deployed with sparse UGW transducer arrays, operated either in an active mode or in a passive mode, both requiring careful decisions on signal feature extraction [8][9][10][11][12][13] and/or damage imaging algorithms. [14][15][16][17][18] Traditional UGW SHM damage imaging techniques rely on knowledge of the material properties of the test part and/ or extraction of physics-based predetermined signal features considered sensitive to damage (e.g., wave amplitude and time of flight). In fiber-reinforced composite parts, and particularly built-up components such as stiffened composite panels, such physics-based knowledge is difficult to obtain.…”

Section: Introductionmentioning

confidence: 99%

“…1–7 Of particular interest are damage detection techniques that can be deployed with sparse UGW transducer arrays, operated either in an active mode or in a passive mode, both requiring careful decisions on signal feature extraction 8–13 and/or damage imaging algorithms. 14–18…”

Section: Introductionmentioning

confidence: 99%

Damage imaging in skin-stringer composite aircraft panel by ultrasonic-guided waves using deep learning with convolutional neural network

Cui

Azuara

Scalea

et al. 2021

Structural Health Monitoring

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The detection and localization of structural damage in a stiffened skin-to-stringer composite panel typical of modern aircraft construction can be addressed by ultrasonic-guided wave transducer arrays. However, the geometrical and material complexities of this part make it quite difficult to utilize physics-based concepts of wave scattering. A data-driven deep learning (DL) approach based on the convolutional neural network (CNN) is used instead for this application. The DL technique automatically selects the most sensitive wave features based on the learned training data. In addition, the generalization abilities of the network allow for detection of damage that can be different from the training scenarios. This article describes a specific 1D-CNN algorithm that has been designed for this application, and it demonstrates its ability to image damage in key regions of the stiffened composite test panel, particularly the skin region, the stringer’s flange region, and the stringer’s cap region. Covering the stringer’s regions from guided wave transducers located solely on the skin is a particularly attractive feature of the proposed SHM approach for this kind of complex structure.

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