Ultrasonic non destructive testing based on sparse deconvolution

Soussen, Charles; Idier, Jérôme; Carcreff, Ewen; Simon, Laurent; Potel, Catherine

doi:10.1088/1742-6596/353/1/012018

Cited by 16 publications

(15 citation statements)

References 23 publications

(24 reference statements)

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“…The method for finding the optimum value of the regularization parameter has been discussed in [12]. The l 1 -norm regularization defined in equation (7) is also known as basis pursuit denoising (BPD) [25].…”

Section: -Norm Regularizationmentioning

confidence: 99%

An adaptive sparse deconvolution method for distinguishing the overlapping echoes of ultrasonic guided waves for pipeline crack inspection

Chang

Zhao

et al. 2017

Meas. Sci. Technol.

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In guided wave pipeline inspection, echoes reflected from closely spaced reflectors generally overlap, meaning useful information is lost. To solve the overlapping problem, sparse deconvolution methods have been developed in the past decade. However, conventional sparse deconvolution methods have limitations in handling guided wave signals, because the input signal is directly used as the prototype of the convolution matrix, without considering the waveform change caused by the dispersion properties of the guided wave. In this paper, an adaptive sparse deconvolution (ASD) method is proposed to overcome these limitations. First, the Gaussian echo model is employed to adaptively estimate the column prototype of the convolution matrix instead of directly using the input signal as the prototype. Then, the convolution matrix is constructed upon the estimated results. Third, the split augmented Lagrangian shrinkage (SALSA) algorithm is introduced to solve the deconvolution problem with high computational efficiency. To verify the effectiveness of the proposed method, guided wave signals obtained from pipeline inspection are investigated numerically and experimentally. Compared to conventional sparse deconvolution methods, e.g. the l 1 -norm deconvolution method, the proposed method shows better performance in handling the echo overlap problem in the guided wave signal.

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Section: -Norm Regularizationmentioning

confidence: 99%

An adaptive sparse deconvolution method for distinguishing the overlapping echoes of ultrasonic guided waves for pipeline crack inspection

Chang

Zhao

et al. 2017

Meas. Sci. Technol.

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“…14). of the material, and it is not forced by adding a sparsity term to the 842 l 2 -norm in the optimization procedure as in [42]. 843 In Fig.…”

Section: Type Of Multilayer Specimen Materialsmentioning

confidence: 99%

A sparse digital signal model for ultrasonic nondestructive evaluation of layered materials

et al. 2015

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“…Fig. 6(a) to (d) present the average error (14) over signals having the same range of maximum correlation, for scans with L = 2 to 5 echoes.…”

Section: B Simulation Containing Several Clustersmentioning

confidence: 99%

“…In Fig. 7 we present the averaged error (14) obtained by cEsa as function of the number of iterations. The initial value (at iteration 0) indicates the error achieved by the initial approximation as described in section II-a.…”

Section: B Simulation Containing Several Clustersmentioning

confidence: 99%

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Cluster-enhanced sparse approximation of overlapping ultrasonic echoes

Mor

Aladjem

Azoulay

2015

IEEE Trans. Ultrason., Ferroelect., Freq. Contr.

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Ultrasonic pulse-echo methods have been used extensively in non-destructive testing of layered structures. In acoustic measurements on thin layers, the resulting echoes from two successive interfaces overlap in time, making it difficult to assess the individual echo parameters. Over the last decade sparse approximation methods have been extensively used to address this issue. These methods employ a large dictionary of elementary functions (atoms) and attempt to select the smallest subset of atoms (sparsest approximation) that represent the ultrasonic signal accurately. In this paper we propose the cluster-enhanced sparse approximation (CESA) method for estimating overlapping ultrasonic echoes. CESA is specifically adapted to deal with a large number of signals acquired during an ultrasonic scan. It incorporates two principal algorithms. The first is a clustering algorithm, which divides a set of signals comprising an ultrasonic scan into groups of signals that can be approximated by the same set of atoms. The second is a two-stage iterative algorithm, which alternates between update of the atoms associated with each cluster, and re-clustering of the signals according to the updated atoms. Because CESA operates on clusters of signals, it achieves improved results in terms of approximation error and computation time compared with conventional sparse methods, which operate on each signal separately. The superior ability of CESA to approximate highly overlapping ultrasonic echoes is demonstrated through simulation and experiments on adhesively bonded structures.

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Ultrasonic non destructive testing based on sparse deconvolution

Cited by 16 publications

References 23 publications

An adaptive sparse deconvolution method for distinguishing the overlapping echoes of ultrasonic guided waves for pipeline crack inspection

An adaptive sparse deconvolution method for distinguishing the overlapping echoes of ultrasonic guided waves for pipeline crack inspection

A sparse digital signal model for ultrasonic nondestructive evaluation of layered materials

Cluster-enhanced sparse approximation of overlapping ultrasonic echoes

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