Publications Internationales

Permanent URI for this collectionhttps://dspace.univ-boumerdes.dz/handle/123456789/13

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Author: search.filters.author.Rahmoune, ChemseddineHas files: No

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    Automated transformer fault diagnosis using infrared thermography imaging, GIST and machine learning technique
    (SAGE, 2022) Mahami, Amine; Rahmoune, Chemseddine; Benazzouz, Djamel
    Condition monitoring of electrical systems is vital in reducing maintenance costs and enhancing their reliability. By focusing on the monitoring of electrical transformers, which play a crucial role in electrical systems and are the main equipment for electrical transmission and distribution, drastic damages, undesirable loss of power and expensive curative maintenance could be avoided. In this paper, a novel noncontact and non-intrusive framework experimental method is used for the monitoring and the diagnosis of transformer faults based on an infrared thermography technique (IRT). The basic structure of this work begins with applying (IRT) to obtain a thermograph of the considered machine. Second, GIST features of the reference image and all images in the image database are extracted. At last, various faults patterns in the transformer are automatically identified using a machine learning method called Support Vector Machine (SVM). The proposed method effectiveness and capacity are evaluated based on the experimental infrared thermography (IRT) images and the diagnosis results by identifying nine sorts of electrical transformer states among which one is healthy and the remaining eight are of short circuit faults in common core winding type, and showing that it can be considered as a powerful diagnostic tool with high Classification Accuracy (CA) and stability compared to other previously used methods
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    Bearing fault detection under time-varying speed based on empirical wavelet transform, cultural clan-based optimization algorithm, and random forest classifier
    (SAGE Publications, 2021) Moussaoui, Imane; Rahmoune, Chemseddine; Zair, Mohamed; Benazzouz, Djamel
    Bearings are massively utilized in industries of nowadays due to their huge importance. Nevertheless, their defects can heavily affect the machines performance. Therefore, many researchers are working on bearing fault detection and classification; however, most of the works are carried out under constant speed conditions, while bearings usually operate under varying speed conditions making the task more challenging. In this paper, we propose a new method for bearing condition monitoring under time-varying speed that is able to detect the fault efficiently from the vibration signatures. First, the vibration signal is processed with the Empirical Wavelet Transform to extract the AM-FM modes. Next, time domain features are calculated from each mode. Then, the features’ set is reduced using the Cultural Clan-based optimization algorithm by removing the redundant and unimportant parameters that may mislead the classification. Finally, an ensemble learning algorithm “Random Forest” is used to train a model able to classify the fault based on the selected features. The proposed method was tested on a time-varying real dataset consisting of three different bearing health states: healthy, outer race defect, and inner race defect. The obtained results indicate the ability of our proposed method to handle the speed variability issue in bearing fault detection with high efficiency
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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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    An early gear fault diagnosis method based on rlmd, hilbert transform and cepstrum analysis
    (Acta Press, 2021) Afia, Adel; Rahmoune, Chemseddine; Benazzouz, Djamel
    Gear fault diagnosis requires an adaptive decomposition method to extract defect signature. As a self-adaptive approach, local mean decomposition (LMD) decomposes the signal to a set of product functions (PFs). However, LMD suffers from two limits: mode mixing and end effect. To overcome this problem, an optimized technique named “robust LMD (RLMD) uses an integrated frame- work: a mirror extending method to find the real extrema in data as well as a self-adaptive tool to select the size of the fixed sub- set for the moving average algorithm for the envelope estimation and finally, a soft sifting stopping criterion to automatically stop the sifting process after determining the most optimum number of sifting iterations. In this article, a combination between RLMD, Hilbert transform (HT), kurtosis and cepstrum analysis is made to monitor a gearbox with chipped tooth using experimental signals. Data are first decomposed using RLMD into a couple of PFs, then HT is applied to each PF to get the envelope for every decom- posed component and highlights the modulated signal related to the gear fault. Subsequently, kurtosis is applied to each envelope to obtain the kurtosis vector for each signal. As healthy vibration characteristics are always taken as a reference, in this article every faulty kurtosis vector is subtracted from the healthy vector, and the PF with the largest kurtosis difference will be selected. Finally, cepstrum analysis is applied to the selected PF to extract the fault signature. Results indicate that our method can detect the chipped tooth in an earlier stage even in a noisy environment