Déconvolution adaptative pour le contrôle non destructif par ultrasons

This thesis deals with the ultrasonic non destructive testing of industrial parts. During real experiments, the signals received by the acoustic transducer are analyzed to detect the discontinuities of the part under test. This analysis can be a difficult task due to digital acquisition, propagation effects and echo overlapping if discontinuities are close. Sparse deconvolution is an inverse method that aims to estimate the precise positions of the discontinuities. The underlying hypothesis of this method is a sparse distribution of the solution, which means there are a few number of discontinuities. In the literature, deconvolution is addressed by a linear time-invariant model as a function of propagation distance, which in reality does not hold.The purpose of this thesis is therefore to develop a model and associated methods in order to cancel the effects of acquisition, propagation and echo overlapping. The first part is focused on the direct model development. In particular, we build a linear time-variant model that takes into account dispersive attenuation. This model is validated with experimental data acquired from attenuative materials. The second part of this work concerns the development of efficient sparse deconvolution algorithms, addressing the minimization of a least squares criterion penalized by a L0 pseudo-norm. Specific algorithms are developed for up-sampled deconvolution, and more robust exploration strategies are built for data containing oscillating waveforms. By using synthetic and experimental data, we show that the developed methods lead to better results compared to standard approaches for a competitive computation time. The proposed methods are then applied to real non destructive testing problems where they confirm their efficiency.Nous nous intéressons au contrôle non destructif par ultrasons des matériaux industriels. En pratique, les signaux réceptionnés par le transducteur ultrasonore sont analysés pour détecter les discontinuités de la pièce inspectée. L'analyse est néanmoins rendue difficile par l'acquisition numérique, les effets de la propagation ultrasonore et la superposition des échos lorsque les discontinuités sont proches. La déconvolution parcimonieuse est une méthode inverse qui permet d'aborder ce problème afin de localiser précisément les discontinuités. Ce procédé favorise les signaux parcimonieux, c'est à dire ne contenant qu'un faible nombre de discontinuités. Dans la littérature, la déconvolution est généralement abordée sous l'hypothèse d'un modèle invariant en fonction de la distance de propagation, modalité qui n'est pas appropriée ici car l'onde se déforme au cours de son parcours et en fonction des discontinuités rencontrées. Cette thèse développe un modèle et des méthodes associées qui visent à annuler les dégradations dues à l'instrumentation et à la propagation ultrasonore, tout en résolvant des problèmes de superposition d'échos. Le premier axe consiste à modéliser la formation du signal ultrasonore en y intégrant les phénomènes propres aux ultrasons. Cette partie permet de construire un modèle linéaire mais non invariant, prenant en compte l'atténuation et la dispersion. L'étape de modélisation est validée par des acquisitions avec des matériaux atténuants. La deuxième partie de cette thèse concerne le développement de méthodes de déconvolution efficaces pour ce problème, reposant sur la minimisation d'un critère des moindres carrés pénalisé par la pseudo-norme L0. Nous avons développé des algorithmes d'optimisation spécifiques, prenant en compte, d'une part, un modèle de trains d'impulsions sur-échantillonné par rapport aux données, et d'autre part le caractère oscillant des formes d'onde ultrasonores. En utilisant des données synthétiques et expérimentales, ces algorithmes associés à un modèle direct adapté aboutissent à de meilleurs résultats comparés aux approches classiques pour un coût de calcul maîtrisé. Ces algorithmes sont finalement appliqués à des cas concrets de contrôle non destructif où ils démontrent leur efficacité

Inspection of Steel Welds Using Total Focusing Method Imaging

The ultrasonic control of steel welds is crucial in the oil and gas transportation industry. The state-of-the-art method is zonal focusing which consists in focusing the ultrasonic beam at specific distances using a wedge in order to check the integrity of specific zones in the weld. Advanced methods such as the total focusing method (TFM) give potentially better images in terms of signal to noise ratio and flaw resolution [1]. Moreover, a complete view of the weld is possible using TFM, contrary to zonal focusing. In this paper, we present a fast implementation of the TFM imaging for weld inspection using wedges. In particular, we propose a GPU implementation that tremendously accelerates the process and makes nearly real-time applications possible. The configuration with two media necessitates an optimization iterative procedure to estimate the proper times of flight, which is implemented in the GPU as well. We also present several skip modes in order to visualize different flaw orientations and locations in the weld (cap, root, etc.). First, we give imaging results on an aluminum block containing artificial flaws such as porosity or lack of fusion. We make a comparison between TFM and conventional imaging, and show that TFM gives better results in terms of image quality (SNR, resolution). Finally, we show results of a real girth weld inspection and demonstrate that the use of TFM is definitely interesting in this context

Blind Sparse Deconvolution of Regularly Spaced Ultrasonic Echoes for Thickness Measurement

We present a method for estimating the thickness of thin materials from ultrasonic data, in the context of coating measurement or thickness estimation of tubes and pipes. When sending an ultrasonic pulse in normal incidence in a homogeneous material, a set of regularly spaced echoes is received. Thickness is then obtained from the estimation of the time delay between echoes. If thin structures are inspected (or if a low frequency transducer is used), then echoes may overlap. Then, visual interpretation is made difficult and standard automatic methods may fail. We propose a blind sparse deconvolution approach to this problem, where data are modeled as the convolution of a spike train with an unknown impulse response that corresponds to the shape of the echoes. The specific structure of the spike train (regularly spaced spikes with geometrically decreasing amplitudes) is taken into account and the echoes are modeled with a frequency modulated Gaussian signal. Joint estimation of all parameters is performed by non-linear least-squares minimization, with specific constraints, initialization and optimization procedure that aim to avoid local minima. Results are presented on simulated data and in application to thickness estimation of aluminum plates with 2mm and 1mm thickness

Adaptative deconvolution for ultrasonic non destructive testing

Nous nous intéressons au contrôle non destructif par ultrasons des matériaux industriels. En pratique, les signaux réceptionnés par le transducteur ultrasonore sont analysés pour détecter les discontinuités de la pièce inspectée. L'analyse est néanmoins rendue difficile par l'acquisition numérique, les effets de la propagation ultrasonore et la superposition des échos lorsque les discontinuités sont proches. La déconvolution parcimonieuse est une méthode inverse qui permet d'aborder ce problème afin de localiser précisément les discontinuités. Ce procédé favorise les signaux parcimonieux, c'est à dire ne contenant qu'un faible nombre de discontinuités. Dans la littérature, la déconvolution est généralement abordée sous l'hypothèse d'un modèle invariant en fonction de la distance de propagation, modalité qui n'est pas appropriée ici car l'onde se déforme au cours de son parcours et en fonction des discontinuités rencontrées. Cette thèse développe un modèle et des méthodes associées qui visent à annuler les dégradations dues à l'instrumentation et à la propagation ultrasonore, tout en résolvant des problèmes de superposition d'échos. Le premier axe consiste à modéliser la formation du signal ultrasonore en y intégrant les phénomènes propres aux ultrasons. Cette partie permet de construire un modèle linéaire mais non invariant, prenant en compte l'atténuation et la dispersion. L'étape de modélisation est validée par des acquisitions avec des matériaux atténuants. La deuxième partie de cette thèse concerne le développement de méthodes de déconvolution efficaces pour ce problème, reposant sur la minimisation d'un critère des moindres carrés pénalisé par la pseudo-norme L0. Nous avons développé des algorithmes d'optimisation spécifiques, prenant en compte, d'une part, un modèle de trains d'impulsions sur-échantillonné par rapport aux données, et d'autre part le caractère oscillant des formes d'onde ultrasonores. En utilisant des données synthétiques et expérimentales, ces algorithmes associés à un modèle direct adapté aboutissent à de meilleurs résultats comparés aux approches classiques pour un coût de calcul maîtrisé. Ces algorithmes sont finalement appliqués à des cas concrets de contrôle non destructif où ils démontrent leur efficacité.This thesis deals with the ultrasonic non destructive testing of industrial parts. During real experiments, the signals received by the acoustic transducer are analyzed to detect the discontinuities of the part under test. This analysis can be a difficult task due to digital acquisition, propagation effects and echo overlapping if discontinuities are close. Sparse deconvolution is an inverse method that aims to estimate the precise positions of the discontinuities. The underlying hypothesis of this method is a sparse distribution of the solution, which means there are a few number of discontinuities. In the literature, deconvolution is addressed by a linear time-invariant model as a function of propagation distance, which in reality does not hold.The purpose of this thesis is therefore to develop a model and associated methods in order to cancel the effects of acquisition, propagation and echo overlapping. The first part is focused on the direct model development. In particular, we build a linear time-variant model that takes into account dispersive attenuation. This model is validated with experimental data acquired from attenuative materials. The second part of this work concerns the development of efficient sparse deconvolution algorithms, addressing the minimization of a least squares criterion penalized by a L0 pseudo-norm. Specific algorithms are developed for up-sampled deconvolution, and more robust exploration strategies are built for data containing oscillating waveforms. By using synthetic and experimental data, we show that the developed methods lead to better results compared to standard approaches for a competitive computation time. The proposed methods are then applied to real non destructive testing problems where they confirm their efficiency

RESOLUTION ENHANCEMENT OF ULTRASONIC SIGNALS BY UP-SAMPLED SPARSE DECONVOLUTION

This paper deals with the estimation of the arrival times of overlapping ultrasonic echoes. We focus on approaches based on discrete sparse deconvolution. Such methods are limited by the time resolution imposed by the model discretization, which is usually considered at the data sampling rate. In order to get closer to the continuous-time model, we propose to increase the time precision by introducing an up-sampling factor in the discrete model. The problem is then recast as a Multiple Input Single Output (MISO) deconvolution problem. Then, we propose to revisit standard sparse deconvolution algorithms for MISO systems. Specific and efficient algorithmic implementation is derived in such setting. Algorithms are evaluated on synthetic data, showing improvements in robustness toward discretization errors and competitive computational time compared to the standard approaches. Index Terms — deconvolution, sparse approximation, MISO systems, ultrasonic data. 1

Nonlinear Beamforming Based on Amplitude Coherence Applied to Ultrasonic Imaging of Coarse-Grained Steels

International audienceAbstract This paper deals with ultrasonic imaging in a nondestructive evaluation (NDE) context. In particular, we are focused on the inspection of coarse-grained steels having a heterogeneous composition that creates structural noise in the ultrasonic signals and images. The standard way to beamform the acquired ultrasonic data is by delay-and-sum (DAS). This method is fast but suffers from low signal-to-noise ratio (SNR) for coarse-grained steel inspection. In this paper, we propose to adapt a coherence-based beamformer called pDAS from the medical imaging community. pDAS beamforming is based on DAS structure but includes p-root and p-power before and after summations, respectively. It results in an enhancement of the coherent summation of signals that improves both resolution and contrast. Coherence-based beamformers are known to enhance information whose acoustic response correlates with geometrical information, that is why they decrease grating lobes and side lobes, specular echoes, reconstruction artifacts, and noise due to multiple scattering. In this paper, the pDAS beamformer is proposed for two common acquisition schemes employed in NDE that are plane wave imaging (PWI) and full matrix capture (FMC). The beamformers have been efficiently implemented for parallel computing on graphics processing unit (GPU) in a context of real-time imaging and fast part scanning in NDE. First, experimental results are presented from an austenitic-ferritic sample from the power generation industry that contains side drilled holes (SDH) with diameter 0.4 mm at several depths. pDAS (for p from two to three) shows improvements in terms of SNR and resolution compared to standard DAS, both in PWI and FMC modalities. We also show that the computation cost of pDAS is equivalent to DAS. A real application on a sample containing a fatigue crack connected to the backwall is exposed. We show that pDAS beamformer can enhance crack response compared to grains, but it also decreases unwanted information such as backwall specular echoes and reconstruction artifacts

Fundamental wave amplitude difference imaging for detection and characterization of embedded cracks

International audienceAn ultrasonic technique for imaging nonlinear scatterers, such as partially-closed cracks, buried in a medium has been recently proposed. The method called fundamental wave amplitude difference (FAD) consists of a sequence of acquisitions with different subsets of elements for each line of the image. An image revealing nonlinear scatterers in the medium is reconstructed line by line by subtracting the responses measured with the subsets of elements from the response obtained with all elements transmitting. In order to get a better insight of the capabilities of FAD, two metallic samples having a fatigue or thermal crack are inspected by translating the probe with ultrasonic beam perpendicular (i.e. parallel) to the crack direction which is the most (i.e. less) favorable case. Each time, the responses of the linear scatterers (i.e. conventional image) and nonlinear scat-terers (i.e. FAD image) are compared in term of intensity and spatial repartition. FAD exhibits higher detection specificity of the crack with a better contrast than conventional ultrasound imaging. Moreover, we observe that both methods give complementary results as nonlinear and linear scatterers are mostly not co-localized. In addition, we show experimentally that FAD resolution in elevation and lateral follows the same rule as the theoretical resolution of conventional ultrasonic technique. Finally, we report that FAD gives the possibility to perform parametric studies which let the opportunity to address the physical mechanisms causing the distortion of the signal. FAD is a promising and reliable tool which can be directly implemented on a conventional open scanner ultrasound device for real-time imaging. This might contribute to its fast and wide spread in the industry

Inspection of Steel Welds Using Total Focusing Method Imaging

The ultrasonic control of steel welds is crucial in the oil and gas transportation industry. The state-of-the-art method is zonal focusing which consists in focusing the ultrasonic beam at specific distances using a wedge in order to check the integrity of specific zones in the weld. Advanced methods such as the total focusing method (TFM) give potentially better images in terms of signal to noise ratio and flaw resolution [1]. Moreover, a complete view of the weld is possible using TFM, contrary to zonal focusing. In this paper, we present a fast implementation of the TFM imaging for weld inspection using wedges. In particular, we propose a GPU implementation that tremendously accelerates the process and makes nearly real-time applications possible. The configuration with two media necessitates an optimization iterative procedure to estimate the proper times of flight, which is implemented in the GPU as well. We also present several skip modes in order to visualize different flaw orientations and locations in the weld (cap, root, etc.). First, we give imaging results on an aluminum block containing artificial flaws such as porosity or lack of fusion. We make a comparison between TFM and conventional imaging, and show that TFM gives better results in terms of image quality (SNR, resolution). Finally, we show results of a real girth weld inspection and demonstrate that the use of TFM is definitely interesting in this context.</p

Blind Sparse Deconvolution of Regularly Spaced Ultrasonic Echoes for Thickness Measurement

We present a method for estimating the thickness of thin materials from ultrasonic data, in the context of coating measurement or thickness estimation of tubes and pipes. When sending an ultrasonic pulse in normal incidence in a homogeneous material, a set of regularly spaced echoes is received. Thickness is then obtained from the estimation of the time delay between echoes. If thin structures are inspected (or if a low frequency transducer is used), then echoes may overlap. Then, visual interpretation is made difficult and standard automatic methods may fail. We propose a blind sparse deconvolution approach to this problem, where data are modeled as the convolution of a spike train with an unknown impulse response that corresponds to the shape of the echoes. The specific structure of the spike train (regularly spaced spikes with geometrically decreasing amplitudes) is taken into account and the echoes are modeled with a frequency modulated Gaussian signal. Joint estimation of all parameters is performed by non-linear least-squares minimization, with specific constraints, initialization and optimization procedure that aim to avoid local minima. Results are presented on simulated data and in application to thickness estimation of aluminum plates with 2mm and 1mm thickness.</p

Approche inverse rapide pour la déconvolution d'images ultrasonores par une PSF variable

National audienceThe proposed method builds ultrasonic images from Full Matrix Capture (FMC) data acquired with a multi-element probe. FMC dataare widely used in Non Destructive Testing and composed of each inter-element reponses. In order to reduce the dimension of the problem, theproposed approach considers the beamformed TFM (Total Focusing Method) image as a back-projection of the ultrasonic data in the image space.We show that the model formulates a 2D deconvolution problem with a varying point spread function (PSF) and we propose an approximation based on the interpolation of the PSF to reduce the computation time. Experimental results on synthetic data are shown, which reveal the accuracyof the proposed model and the reduction of the computing time compared to the reconstruction based on the FMC data.Nous proposons une méthode de reconstruction d'images ultrasonores à partir de données issues de sondes multi-éléments, compo-sées des réponses de chaque couple émetteur-récepteur (Full Matrix Capture, FMC), typiquement utilisées pour le contrôle non destructif des matériaux. Afin de réduire la taille du problème, nous proposons une approche où les « données » sont constituées de l'image refocalisée a pos-teriori dite TFM (Total Focusing Method), laquelle est interprétée comme une rétro-projection des mesures dans l'espace image. Nous montrons que ce problème s'apparente à un problème de déconvolution 2D à réponse impulsionnelle (PSF) variable et proposons une approximation basée sur l'interpolation de PSF pour limiter le coût de calcul. Des résultats sur des données synthétiques valident la pertinence de ce modèle ainsi que la réduction du temps de calcul par rapport à la reconstruction basée sur les données FMC brutes