Publications Scientifiques

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    Prediction of mass adhesive damage based on the Rousselier model: Experimental and numerical analysis
    (Elsevier Ltd, 2024) Houari, Amin; Madani, Kouider; Belhouari, Mohamed; Amroune, Salah; Cohendoz, Stéphane; Preaudeau, Bruno; Feaugas, Xavier; Campilho, Raul DSG.
    The study of the mechanical strength of adhesives remains an important area of research for researchers. These adhesives must be prepared in the form of mass test pieces to characterize them under different mechanical stresses. However, during the preparation of the test pieces several defects are likely to be present, namely air bubbles, cavities, or impurities. The behavior of the adhesive differs depending on the presence of one of these defects and, in most cases, the real behavior of the adhesive is not precisely known. For this purpose, several tests are necessary to have a close estimate of the adhesive's behavior. To numerically model the behavior of the adhesive it is necessary to consider the presence of these types of defects. This paper proposes a damage criterion based on the Rousselier model, which describes the damage due to crack growth from the presence of cavities in an adhesive, assumed as a ductile material. The proposed damage model was developed and implemented in a user-defined subroutine in the ABAQUS finite element code. Other damage models integrated into ABAQUS were used. In addition, the extended finite element method (XFEM) was used in the numerical simulations to study automatic damage modelling by the appearance and propagation of cracks in highly stressed areas. The main objective of this work is an analysis by the finite element method to determine the elastoplastic behavior coupled with the damage in the mass adhesive, considering the size, position, and shape of the defect (porosities) by the proposed models. Initially, experimental tests were carried out on mass specimens of adhesive to characterize the tensile response and to determine their mechanical properties depending on the position and size of the defect, which may exist in the specimen following its fabrication. The numerical results were validated by uniaxial tensile tests on the mass adhesive. Comparisons with the damage models integrated into ABAQUS have proven their effectiveness in predicting the behavior of the adhesive in the presence of a cavity.
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    Impact of heterogeneous cavities on the electrical constraints in the insulation of high-voltage cables
    (Polish Society of Technical Diagnostics, 2023) Bakri, Badis; Benguesmia, Hani; Mira, Aya; M'ziou, Nassima
    The main insulation layer is the most important layer of the high-voltage cable, and the quality of this material directly affects the life of the cable. It is also known that contamination, porosity and associated partial discharges in the insulation can affect the service life of cables. In this paper, we use the COMSOL Multiphysics software, which is based on the finite element method in AC/DC, 2D electrostatic. Our study shows the effect of heterogeneous cavities on the functioning of electrical cables. This work contains the study of electric field distribution and potential of a model of high voltage cable; we took into account the absence and the presence of heterogeneous cavities. The study was conducted using numerical results with mathematical validation. The obtained results are considered satisfactory, favorable and very promising
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    Gear fault detection, identification and classification using MLP neural network
    (Springer, 2023) Afia, Adel; Ouelmokhtar, Hand; Gougam, Fawzi; Touzout, Walid; Rahmoune, Chemseddine; Benazzouz, Djamel
    Gear fault detection, identification and classification are highly complicated tasks, as the faults which affect gearboxes tend to share similar frequency signatures. Therefore, load and speed changes in a rotating machinery inevitably provide inaccurate results. However, identifying the fault remains critical, as each individual gear fault influences overall mechanism operation in different manners. Therefore, defect identification and classification appear as the hardest challenge for a geared systems. An automatic method to detect, identify and classify different gear failures is presented in this paper. The intelligent approach consists of a combination of MODWPT, entropy and MLPNN. MODWPT was developed to decompose the signals with a uniform frequency bandwidth. Entropy is employed to build the feature matrix in the feature extraction phase. Then, MLP offers a very efficient classification tool for features classification stage. Based on data sets taken from a gearbox bench test with a good and five varied gear states under various loads and speeds, experimental results presented the efficiency of our technique
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    Strength prediction of a steel pipe having a hemi-ellipsoidal corrosion defect repaired by GFRP composite patch using artificial neural network
    (Elsevier, 2023) Oulad Brahim, Abdelmoumin; Belaidi, Idir; Khatir, Samir; Le Thanh, Coung; Mirjalili, Seyedali; Magd, Abdel Wahab
    Local stress concentration occurs when faults are present in pipelines under pressure. An example of such defects is the problem of corrosion caused by the environment in the field of pipeline installation. In the first part of this paper, we attempt to model the corrosion in the hemi-ellipsoidal form in order to study the locations of stress concentration in the specimens by several experimental cases and their influence on the stress resistance. The Gurson-Tvergaard-Needleman (GTN) mesoscopic damage model is used to simulate the specimens with good accuracy. In the second part, the investigation is extended to a pipe under static pressure with and without the presence of a glass fibre reinforced polymer (GFRP) composite patch. The maximum stress and percent stress reduction in a defected pipe with a hemi-ellipsoidal defect are determined using a 3D finite element model. This part examines the impact of the geometry of the composite patches on the percentage reduction of the maximum stresses in a section of pipeline subjected to static pressure. In the third part, the stresses and the percentage reduction in the maximum stresses are predicted using an artificial neural network (ANN). An inverse problem using ANN and Jaya algorithm is proposed to predict the group level of different sizes of defects under composite patches based on the maximum stress and percentage reduction of stress that the pipe withstands. The new method relates directly to real-world pipeline construction and repair applications. It could be also used for structural safety monitoring
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    New gear fault diagnosis method based on MODWPT and neural network for feature extraction and classification
    (ASTM International, 2019) Afia, Adel; Rahmoune, Chemseddine; Benazzouz, Djamel; Merainani, Boualem; Fedala, Semchedine
    Gear fault diagnosis using vibration signals has become the subject of intensive studies to detect any sudden failure. However, these signals exhibit nonlinear and nonstationary behaviors when the rotating machine operates under multiple working conditions. Furthermore, fault features extraction and classification of multiple gear states are always unsatisfactory and considered as a huge task. This is the main reason that motivates us to develop a new intelligent gear fault diagnosis method in order to automatically identify and classify several kinds of gear defects under different work conditions. So in this article, we propose a combination between the maximal overlap discrete wavelet packet transform (MODWPT), entropy indicator, and a multilayer perceptron (MLP) neural network as a new automatic fault diagnosis approach. MODWPT decomposes the data signal into several components using a uniform frequency bandwidth. Each decomposed component is selected to extract feature vector using entropy indicator. Finally, MLP provides a powerful automatic tool for identifying and classifying the aforementioned extracted features. Experimental vibration signals of healthy gear; gear with general surface wear; gear with chipped tooth in length; gear with chipped tooth in width; gear with missing tooth; and gear with tooth root crack are recorded under fifteen different work conditions to test the effectiveness of the suggested technique. Experimental results affirm that our proposed approach can successfully detect, identify, and classify the gear fault pattern in all cases
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    Gear fault diagnosis using Autogram analysis
    (Sage, 2018) Afia, Adel; Rahmoune, Chemseddine; Djamel, Benazzouz
    Rotary machines consist of various devices such as gears, bearings, and shafts that operate simultaneously. As a result, vibration signals have nonlinear and non-stationary behavior, and the fault signature is always buried in overwhelming and interfering contents, especially in the early stages. As one of the most powerful non-stationary signal processing techniques, Kurtogram has been widely used to detect gear failure. Usually, vibration signals contain a relatively strong non-Gaussian noise which makes the defective frequencies non-dominant in the spectrum compared to the discrete components, which reduce the performance of the above method. Autogram is a new sophisticated enhancement of the conventional Kurtogram. The modern approach decomposes the data signal by Maximal Overlap Discrete Wavelet Packet Transform into frequency bands and central frequencies called nodes. Subsequently, the unbiased autocorrelation of the squared envelope for each node is computed to select the node with the highest kurtosis value. Finally, Fourier transform is applied to that squared envelope to extract the fault signature. In this article, the proposed method is tested and compared to Fast Kurtogram for gearbox fault diagnosis using experimental vibration signals. The experimental results improve the detectability of the proposed method and affirm its effectiveness
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