Electronique

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Texture Analysis for Flaw Detection in Ultrasonic Images

Ahmed KECHIDA, Redouane DRAI, Abderrezak GUESSOUM  (2012)
Publication

In this paper, we present two approaches for flaw detection in TOFD (Time of Flight Diffraction) images based on texture features. Texture is one of the most important features used in recognizing patterns in an image. The paper describes texture features by two methods: Multiresolution analysis such as wavelet transforms and Gabor filters bank. The two-dimensional wavelet transform is used to decompose the input image into a multiresolution framework. The textural statistical parameters are used to allow the choice of the decomposition channel. The Gabor filter is a Gaussian kernel function modulated by a sinusoidal plane wave. All Gabor filters can be generated from one mother wavelet by dilation and rotation. These filters represent an appropriate choice for tasks requiring simultaneous measurement in both space and frequency domains. The most relevant features are optimized by Principal Components Analysis (PCA) and used as input data on a Fuzzy C-Mean clustering classifier. We use two classes: ‘defects’ or ‘no defects’. The proposed approach is tested on the TOFD image achieved in an industrial field. Voir les détails

Mots clés : Texture analysis, NDE, TOFD image, Wavelet transform, Gabor filters, Fuzzy logic, PCA

Signal processing for the detection of multiple imperfection echoes drowned in the structural noise

Redouane DRAI, Abdessalem BENAMMAR, Amar BENCHAALA  (2004)
Publication

In this work, we propose to develop algorithms based on the split spectrum processing method associated with the multi-steps method based on “Group delay moving entropy” (GDME) allowing detecting and locating multiple imperfection echoes drowned in the structural noise of materials. In fact, GDME is based on the fact that defect echoes have a constant group delay while the noise has a random group delay. The investigation is performed with 4 known defect echoes with different characteristics (position, center frequency and bandwidth). The defect echo frequency is varied around the frequency of the input signal in order to evaluate, by signal to noise ratio calculation, the robustness of the detection method. The grain noise signal is generated first, by a simple clutter model which consider the noise, in the time domain, as the superimposed of signal coming from backscaterers in the medium and second, experimentally by a material with coarse grains. Voir les détails

Mots clés : Ultrasonic NDE, split spectrum processing, Signal to noise ratio, Structure noise

Ultrasonic Flaw Detection in Composite Materials Using SSP-MPSD Algorithm

Abdessalem BENAMMAR, Redouane DRAI  (2014)
Publication

Due to the inherent inhomogeneous and anisotropy nature of the composite materials, the detection of internal defects in these materials with non-destructive techniques is an important requirement both for quality checks during the production phase and in service inspection during maintenance operations. The estimation of the time-of-arrival (TOA) and/or time-of-flight (TOF) of the ultrasonic echoes is essential in ultrasonic non-destructive testing (NDT). In this paper, we used split-spectrum processing (SSP) combined with matching pursuit signal decomposition (MPSD) to develop a dedicated ultrasonic detection system. SSP algorithm is used for Signal-to-Noise Ratio (SNR) enhancement, and the MPSD algorithm is used to decompose backscattered signals into a linear expansion of chirplet echoes and estimate the chirplet parameters. Therefore, the combination of SSP and MPSD (SSP-MPSD) presents a powerful technique for ultrasonic NDT. The SSP algorithm is achieved by using Gaussian band pass filters. Then, MPSD algorithm uses the Maximum Likelihood Estimation. The good performance of the proposed method is experimentally verified using ultrasonic traces acquired from three specimens of carbon fibre reinforced polymer multi-layered composite materials (CFRP). Voir les détails

Mots clés : Non-Destructive testing, Ultrasonics, Carbon fibre, Defects, Signal processing

Detection of delamination defects in CFRP materials using ultrasonic signal processing

Abdessalem BENAMMAR, Redouane DRAI, Abderrezak GUESSOUM  (2008)
Publication

In this paper, signal processing techniques are tested for their ability to resolve echoes associated with delaminations in carbon fiber-reinforced polymer multi-layered composite materials (CFRP) detected by ultrasonic methods. These methods include split spectrum processing (SSP) and the expectation–maximization (EM) algorithm. A simulation study on defect detection was performed, and results were validated experimentally on CFRP with and without delamination defects taken from aircraft. Comparison of the methods for their ability to resolve echoes are made. Voir les détails

Mots clés : Ultrasonic NDE, composite materials, CFRP, SSP, Deconvolution, EM algorithm

Ultrasonic Inspection of Composite Materials Using Minimum Entropy Deconvolution

Abdessalem BENAMMAR, Redouane DRAI, Abderrezak GUESSOUM  (2010)
Publication

In this work, the Minimum Entropy Deconvolution (MED) method, developed for ultrasonic signals, is used to address the problem of delamination defect detection in Composite Materials. Standard deconvolution techniques suppose that the wavelet is minimum phase but generally make no assumptions about the amplitude distribution of the primary reflection coefficient sequence. For a white reflection sequence the assumption of a Gaussian distribution means that recovery of the true phase of the wavelet is impossible; however, a non-Gaussian distribution in theory allows recovery of the phase. It is generally recognized that primary reflection coefficients typically have a non-Gaussian amplitude distribution. The minimum entropy deconvolution (MED) method supposes whiteness but seek to exploit the non-Gaussianity. This method do not assume minimum phase. The deconvolution filter is defined by the maximization of a function called the objective. The algorithm is tested on simulated data and also tested on real ultrasonic data from multilayered composite materials. Voir les détails

Mots clés : blind deconvolution, composite, Nondestructive Testing (NDT), Ultrasonic

Time-frequency and wavelet transform applied to selected problems in ultrasonics NDE

Redouane DRAI, Mohamed KHELIL, Amar BENCHAALA  (2002)
Publication

In this paper, we contribute by the development of some signal processing in order to enhance the sensibility of flaw detection, to measure thin materials thickness and to characterize defects in nature (planar or volumetric). Features for discrimination of detected echos are extracted in time domain, spectral domain and discrete wavelet representation. Compact feature vector obtained is then classified by different methods: K nearest neighbour algorithm, statistical Bayesian algorithm and artificial neural network. Mallat decomposition algorithm is also developed in order to enhance flaw detectability. Finally, time frequency algorithms based on STFT, Wigner–Ville, Gabor transform are developed and applied to thickness measurements of materials with small thickness. Voir les détails

Mots clés : NDT/NDE, Ultrasonic, time frequency representation, Wavelet transform

Elaboration of some signal processing algorithms in ultrasonic techniques: application to materials NDT

Redouane DRAI, F. SELLIDJ, Mohamed KHELIL, Amar BENCHAALA  (2000)
Publication

In ultrasonic techniques, information on defect characterization possibilities has required more evolved technique development than classical methods. To obtain a high probability of defect detection, these methods use signal-processing algorithms in order to enhance the signal-to-noise ratio. These methods are also used to discriminate between planar and volumetric defects. In this paper, some signal-processing algorithms are developed and implemented on a computer to allow their utilization in real-time processing of ultrasonics NDT results. Voir les détails

Mots clés : Cross-correlation function, Hilbert transform, NDT, Split spectrum techniques, Ultrasonics

Ultrasonic flaw detection using threshold modified S-transform

Abdessalem BENAMMAR, Redouane DRAI, Abderrezak GUESSOUM  (2014)
Publication

Interference noising originating from the ultrasonic testing defect signal seriously influences the accuracy of the signal extraction and defect location. Time–frequency analysis methods are mainly used to improve the defects detection resolution. In fact, the S-transform, a hybrid of the Short time Fourier transform (STFT) and wavelet transform (WT), has a time frequency resolution which is far from ideal. In this paper, a new modified S-transform based on thresholding technique, which offers a better time frequency resolution compared to the original S-transform is proposed. The improvement is achieved by the introduction of a new scaling rule for the Gaussian window used in S-transform. Simulation results are presented and show correct time frequency information of multiple Gaussian echoes under low signal-to-noise ratio (SNR) environment. In addition, experimental results demonstrate better and reliable detection of close echoes drowned in the noise. Voir les détails

Mots clés : Flaw detection, Ultrasonic signal, Time–frequency signal analysis, Modified S-transform

Multi-objective GA optimization of fuzzy penalty for image reconstruction from projections in X-ray tomography

Ali Mohamed Tahar GOUICEM, Khier BENMAHAMMED, Redouane DRAI, Mostapha YAHI, Abdelmalik TALEB-AHMED  (2012)
Publication

This paper concerns X-ray tomography image reconstruction of an object function from few projections in Computed Tomography (CT). The problem is so ill-posed that no classical method can give satisfactory result. We have investigated a new combined method for penalized-likelihood image reconstruction that combines the fuzzy penalty function (FP) and GA (genetic algorithm) optimization. The proposed algorithm does not suffer from the same problem as that of ML EM (maximum likelihood expectation maximization) algorithm, and it converges rapidly to a low noisy solution even if the iteration number is high, and gives global estimation not a local one like in classical algorithm such as gradient, to the problem of determining object parameters. The method was tested and validated on datasets of synthetic and real image. Voir les détails

Mots clés : Computed tomography, Non-Destructive testing, Analytic estimation, Bayesian inference and estimation, Fuzzy inference, Genetic optimization

Design and deformation behavior of high strength Fe–Mn–Al–Cr–C duplex steel

TAHAR SAHRAOUI, Mohamed HADJI, Mostapha YAHI  (2009)
Publication

The influence of cold rolling reduction on microstructures and mechanical properties at room temperature of the duplex Fe–28Mn–7Al–5Cr–0.3C steel was investigated. In the Fe–28Mn–7Al alloy system, the duplex microstructure was obtained by lowering the carbon content to about 0.3 wt.%. The steel was austenito-ferritic with a low to moderate stacking fault energy. Two thermomechanical cycles were performed, which included cold rolling/annealing at 1100 °C, and cold rolling/annealing at 1100 °C/cold rolling/annealing at 1000 °C. The effects produced by cold rolling on the duplex steel were grain refinement and different strain-induced marks within the ferrite and austenite phases. They were easily observed within the austenite phase at a relatively smaller reduction than within the ferrite phase. Mechanical twinning plays a dominant role within the austenite phase during deformation at room temperature, resulting in extreme mechanical properties. No edge or longitudinal cracks were observed during cold rolling of the duplex steel. Voir les détails

Mots clés : Duplex steel, Cold rolling, Annealing, Microstructures, Austenite, Ferrite