Nombre total de résultats : 509
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Propriétés Optique de TiO2 et Application de la Méthode de Swanepoel pour la Détermination de l’Épaisseur Optique et de l’Indice de Réfraction

K. Bedoud, H. MERABET  (2019)

Dans ce travail, des nano-films de dioxyde de titane « TiO2 » ont été déposés par pulvérisation cathodique en utilisant une cible en céramique de Ti pur de 3" de diamètre et 0,250" d'épaisseur avec une pureté de 99,99% sur des substrats en verre à des épaisseurs (e) différents. Nous visons par ce travail d’étudier l’effet de l’épaisseur sur les propriétés optiques de TiO2 nano films. Pour cela, nous avons utilisé la spectroscopie de transmittance optique UV-Visible pour la caractérisation optique. La variation du gap optique des films est inversement proportionnelle à la variation de l’épaisseur de 3,6 eV à 3,8 eV, respectivement. Pour la détermination de l'indice de réfraction et l'épaisseur du film nous avons utilisé la méthode proposée par Swanepoel, qui s’articule sur l’utilisation des franges d’interférence. On observe que, l'indice de réfraction n augmente avec l’augmentation de l'épaisseur de la couche déposée. Voir les détails

Mots clés : couches minces, pulvérisation, semi-conducteur, TiO2, nano-films, UV-Vis, épaisseur, indice de réfraction, Swanepoel

Real-Time Switches Fault Diagnosis for Voltage Source Inverter Driven Induction Motor Drive

H. MERABET, T. BAHI, K. Bedoud, D. DRICI  (2019)

Induction machine is the frequently used for electrical drive applications in almost many industrial processes due to its simple and robust construction. Speed control of induction machine is required depending on the type of application. Speed of the induction motor can be varied by varying frequency or by variation of the terminal voltage. Variable voltage can be fed to induction machine using the voltage source inverter which is found efficient technique of controlling induction motor drive. The potential faults that occur in inverter are the open and short circuit switch fault. The cost of this schedule can be high, and this justifies the development of fault diagnostic methods. In this paper we present a reliable strategy for diagnosis and detection of open and short circuit switch faults in plush width modulation of voltage source inverter (PWM-VSI) using the fuzzy logic approach. The principle of the proposed approach is based on the acquisition of stator currents, to calculate the average absolute values of currents (AAVC), which allows the real-time detection and localization of inverter IGBT open or short-circuit faults using just the motor phase currents. A model of the system is built using MATLAB/SIMULINK. Simulation results are presented showing the monitoring approach performance under distinct operating conditions. Voir les détails

Mots clés : open circuit fault, short circuit fault, Fuzzy logic, modeling, simulation

Rolling bearing fault diagnosis based on improved complete ensemble empirical mode of decomposition with adaptive noise combined with minimum entropy-deconvolution


The vibration signals provide useful information about the state of rolling bearing and the diagnosis of the faults requires an accurate analysis of these signals. Several methods have been developed for diagnosing rolling bearing faults by vibration signal analysis. In this paper, we present an improvement of the technique Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN), this technique is combined with the Minimum Entropy Deconvolution (MED) and the correlation coefficient to diagnose defects. First, the vibration signal was decomposed by the improved CEEMDAN decomposition into several oscillatory modes called Intrinsic Mode Function (IMF). After calculation of the correlation coefficients between the original signal and their IMFs, the modes with higher coefficients are selected as the relevant modes. Secondly, the MED technique is applied to the selected modes in order to improve the sensitivity of the scalar and frequency indicators of faults detection. Finally, kurtosis and envelope analysis are used to detect and locate the defect position. The simulation is carried out using the Case Western University data base and the results obtained show that the proposed method provides very good results for the early detection and diagnosis of defects and can efficiently extract the defective characteristics of the rolling bearing. Voir les détails

Mots clés : vibration signal, rolling bearing fault, complementary ensemble empirical mode decomposition, coefficient correlation, minimum entropy deconvolution, Kurtosis, Envelope analysis

Enhancement of rolling bearing fault diagnosis based on improvement of empirical mode decomposition denoising method


Signal processing is a widely used tool in the field of monitoring and diagnosis of rolling bearing faults. The vibration signals of rolling bearing contain important information which can be used for early detection and diagnosis of faults. These signals are usually noisy and masked by other sources and therefore the information about the fault can be lost. In this work, we propose an enhancement of rolling bearing fault diagnosis based on the improvement of empirical mode decomposition (EMD) denoising method. This method is made to extract the useful fault signal in order to use the detection indicators such as the kurtosis and the envelope spectrum. Firstly, EMD is applied to the vibration signals to obtain a series of functions called the intrinsic mode functions (IMFs). Secondly, we present an approach based on the energy content of each mode to determine the trip point which allows selecting the relevant modes. The singular selected IMFs are determined by comparing the average energy of all the unselected IMFs with the energy of each selected IMFs; then, an optimized thresholding operation is performed to denoise these IMFs. Finally, the kurtosis and spectral envelope analysis were investigated for early detection and localization of the fault position. Different experimental data are used to validate the effectiveness of the proposed method. The obtained results showed that the proposed method is more efficient and more sensitive to the early detection and diagnosis of rolling bearing faults than the conventional denoising method. Voir les détails

Mots clés : Vibration analysis, bearing Fault diagnosis, EMD, threshold Denoising, energy, Relevant mode selection, envelope, Kurtosis

Rolling Bearing Fault Diagnosis Based on an Improved Denoising Method Using the Complete Ensemble Empirical Mode Decomposition and the Optimized Thresholding Operation


Vibration signals are widely used in monitoring and diagnosing of rolling bearing faults. These signals are usually noisy and masked by other sources, which may therefore result in loss of information about the faults. This paper proposes an improved denoising method in order to enhance the sensitivity of kurtosis and the envelope spectrum for early detection of rolling bearing faults. The proposed method is based on a complete ensemble empirical mode decomposition with an adaptive noise (CEEMDAN) associated with an optimized thresholding operation. First, the CEEMDAN is applied to the vibration signals to obtain a series of functions called the intrinsic mode functions (IMFs). Second, an approach based on the energy content of each mode and the white noise characteristic is proposed to determine the trip point in order to select the relevant modes. By comparing the average energy of all the unselected IMFs with the energy of each selected IMFs, the singular IMFs are selected. Third, an optimized thresholding operation is applied to the singular IMFs. Finally, the kurtosis and the envelope spectrum are used to test the effectiveness of the proposed method. Different experimental data of the Case Western Reserve University Bearing Data Center are used to validate the effectiveness of the proposed method. The obtained experimental results illustrate well the merits of the proposed method for the diagnosis and detection of rolling bearing faults compared to those of the conventional method. Voir les détails

Mots clés : Vibration analysis, bearing Fault diagnosis, CEEMDAN, Denoising, thresholding operation, envelope, Kurtosis

Asymmetric Generalized Gaussian Distribution Parameters Estimation based on Maximum Likelihood, Moments and Entropy

Nafaa Nacereddine, Aicha Baya Goumeidane  (2019)
Article de conférence

In this paper, we address the problem of estimating the parameters of Asymmetric Generalized Gaussian Distribution (AGGD) using three estimation methods, namely, Maximum Likelihood Estimation (MLE), Moment Matching Estimation (MME) and Entropy Matching Estimation (EME). For this purpose, these methods are applied on an unimodal histogram fitting of an image corrupted with AGGD noise. Experiments show that the effectiveness of each method comparatively to the other one depends on the variation range of the shape factor. Voir les détails

Mots clés : Asymmetric generalized Gaussian distribution, Parameter estimation, maximum likelihood, Moments, Entropy

Impact of the inhomogeneous structure of the active layer on the transfer characteristic of polysilicon TFT's

Hadjira Tayoub, asmaa bensmain, Baya Zebentout, Zineb Benamara  (2012)

Recently polycrystalline silicon (pc-Si) thin film transistors (TFT's) have emerged as the devices of choice for many applications. The TFT's made of a thin un-doped polycrystalline silicon film deposited on a glass substrate by the Low Pressure Chemical Vapor Deposition technique LPCVD have limits in the technological process to the temperature <; 600°C. The benefit of pc-Si is to make devices with large grain size. Unfortunately, according to the conditions during deposition, the pc-Si layers can consist of a random superposition of grains of different sizes, where grains boundaries parallels and perpendiculars appear. In this paper, the transfer characteristics IDS-VGS are simulated by solving a set of two-dimensional (2D) drift-diffusion equations together with the usual density of states (DOS: exponential band tails and Gaussian distribution of dangling bonds) localized at the grains boundaries. The impact of thickness of the active layer on the distribution of the electrostatic potential, the effect of density of intergranular traps states and grain size on the TFT's transfer characteristics IDS-VGS have been also investigated. Voir les détails

Mots clés : Transistor TFT, 2D simulation, heterogeneous structure, Grain Size, transfer characteristic


TAYOUB Hadjira (2011)
Mémoire de magister

L’objectif du thème proposé est consacré à une étude sur la construction d’une structure hétérogène de silicium polycristallin permettant de rendre compte les caractéristiques courant-tension du transistor MOSà canal N fabriqué à base de ce matériau. La couche de silicium polycristallin réelle contient de grain et de jointsde grain répartis aléatoirement. Donc pour modéliser plus simplement cette couche, un modèle géométrique 2Dà permet la mise en évidence d’un certain nombre de joints de grain perpendiculaires et parallèles à la surfacede croissance.Pour cela, une simulation approfondie basée sur la résolution numérique, à deux dimensions, des équationsdécrivant le transport dans les dispositifs à semi-conducteurs (équation de Poisson et les deux équationsde continuité des électrons et des trous) est utilisée en tenant en compte de la particularité des propriétés électriqueset physiques du silicium polycristallin. Dans cette simulation, on analyse la sensibilité des caractéristiques de transfert IDS(VGS) en fonction de la structure granulaire du canal, le nombre de joints de grains, défauts intergranulaires et à l’interface etc… Voir les détails

Mots clés : Silicium polycristallin, Structure hétérogène, Transistor TFT, Techniques basses températures, Simulation numérique 2D

Unsupervised weld defect classification in radiographic images using multivariate generalized Gaussian mixture model with exact computation of mean and shape parameters

Nafaa Nacereddine, Aicha Baya Goumeidane, Djemel Ziou  (2019)

In industry, the welding inspection is considered as a mandatory stage in the process of quality assurance/quality control. This inspection should satisfy the requirements of the standards and codes governing the manufacturing process in order to prevent unfair harm to the industrial plant in construction. For this purpose, in this paper, a software specially conceived for computer-aided diagnosis in weld radiographic testing is presented, where a succession of operations of preprocessing, image segmentation, feature extraction and finally defects classification is carried out on radiographic images. The last operation which is the main contribution in this paper consists in an unsupervised classifier based on a finite mixture model using the multivariate generalized Gaussian distribution (MGGD). This classifier is newly applied on a dataset of weld defect radiographic images. The parameters of the nonzero-mean MGGD-based mixture model are estimated using the Expectation-Maximization algorithm where, exact computations of mean and shape parameters are originally provided. The weld defect database represent four weld defect types (crack, lack of penetration, porosity and solid inclusion) which are indexed by a shape geometric descriptor composed of geometric measures. An outstanding performance of the proposed mixture model, compared to the one using the multivariate Gaussian distribution, is shown, where the classification rate is improved by 3.2% for the whole database, to reach more than 96%. The efficiency of the proposed classifier is mainly due to the flexible fitting of the input data, thanks to the MGGD shape parameter. Voir les détails

Mots clés : Mixture model, Multivariate GGD, radiography, weld defect, classification

Fast Adapting Mixture Parameters Schemes for Probability Density Difference-Based Deformable Model

Aicha Baya Goumeidane, Nafaa Nacereddine  (2019)

This paper presents a new region-driven active contour using the pdf difference to evolve. The pdf estimation is done via a new and fast Gaussian mixture model (GMM) parameters updating scheme. The experiments performed on synthetic and X-ray images have shown not only an accurate contour delineation but also outstanding performance in terms of execution speed compared to the GMM estimation based on EM algorithm and to non-parametric pdf estimations. Voir les détails

Mots clés : Active contour, Adaptive mixture, GMM parameters update