Browsing by Author "Ikhlef, Boualem"

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Contribution à l’étude de la supervision industrielle automatique dans un environnement SCADA
(2009) Ikhlef, Boualem
Répondant aux besoins de la qualité, de la quantité de la production et de la concurrence du marché économique, les industriels ont tendance à améliorer et à élargir leurs installations et deviennent ainsi de plus en plus complexes, contribuant en même temps à augmenter les risques de pannes qui peuvent survenir sur le fonctionnement de l’installation et à diminuer la sécurité du personnel et de l’environnement. A cet effet beaucoup de méthodes de surveillance ont été développées ces dernières se divisent en deux grandes familles, on retrouve des méthodes à base de modèle, et des méthodes à base des données historiques du système. D’autres techniques de supervision ont été développées pour les installations à haut risque, qui consistent à superviser à partir d’un poste de pilotage qui se situe très loin du site supervisé, cette technique de supervision appartient à la supervision dans un environnement SCADA Dans le premier chapitre de ce travail, on a présenté l’architecture d’un système industriel, quelques concepts généraux de la supervision, les risques qui menacent la sureté industrielle et un petit aperçu sur l’environnement SCADA. Dans le deuxième chapitre, on a présenté un état de l’art des deux catégories de méthodes de surveillance qui existent ; les méthodes à base de modèle et les méthodes sans modèle. Dans le troisième chapitre, on a présenté l’approche structurelle et l’estimation paramétrique avec des exemples illustratifs et une comparaison entre ces deux approches Dans le quatrième chapitre, on a généré les algorithmes de surveillance d’un système à trois réservoirs, en utilisant l’approche structurelle, puis on les a implémenté dans l'induSoft Web Studio, un logiciel de supervision qui fonctionne dans l’environnement SCADA, et on a aussi réalisé une supervision et un contrôle à distance
Detection and identification of defects in gearbox systems using artificial intelligence based techniques
(Université M'Hamed Bougara Boumerdès : Faculté de Technologie, 2023) Ikhlef, Boualem; Benazzouz, Djamel(Directeur de thèse)
Gearboxes are massively utilized in nowadays industries due to their huge importance in power transmission; hence, their defects can heavily affect the machines performance. Therefore, many researchers are working on gearboxes fault detection and classification. However, most of the works are carried out under constant speed conditions, while gears usually operate under varying speed and torque conditions, making the task more challenging. In this work, we propose a new method for gearboxes condition monitoring that is efficiently able to reveal the fault from the vibration signatures under varying operating condition. First, the vibration signal is processed with the Maximal Overlap Discrete Wavelet Packet Transform (MODWPT) to extract the modes. Next, time domain features are calculated from each mode. Then the features set are reduced using the Ant colony optimization algorithm (ACO) by removing the redundant and unimportant parameters that may mislead the classification. Finally, an ensemble learning algorithm Random Forest (RF) is used to train a model able to classify the fault based on the selected features. The innovative aspect about this method is that, unlike other existing methods, ACO is able to optimize not only the features but also the parameters of the classifier in order to obtain the highest classification accuracy. The proposed method was tested on varying operating condition real dataset consisting of six different gearboxes. In the aim to prove the performance of our method, it had been compared to other conventional methods. The obtained results indicate its robustness, and its accuracy stability to handle the varying operating condition issue in gearboxes fault detection and classification with high efficiency
Gearboxes fault detection under operation varying condition based on MODWPT, Ant colony optimization algorithm and Random Forest classifier
(SAGE Publications, 2021) Ikhlef, Boualem; Rahmoune, Chemseddine; Bettahar, Toufik; Benazzouz, Djamel
Gearboxes are massively utilized in nowadays industries due to their huge importance in power transmission; hence, their defects can heavily affect the machines performance. Therefore, many researchers are working on gearboxes fault detection and classification. However, most of the works are carried out under constant speed conditions, while gears usually operate under varying speed and torque conditions, making the task more challenging. In this paper, we propose a new method for gearboxes condition monitoring that is efficiently able to reveal the fault from the vibration signatures under varying operating condition. First, the vibration signal is processed with the Maximal Overlap Discrete Wavelet Packet Transform (MODWPT) to extract the AM-FM modes. Next, time domain features are calculated from each mode. Then the features set are reduced using the Ant colony optimization algorithm (ACO) by removing the redundant and unimportant parameters that may mislead the classification. Finally, an ensemble learning algorithm Random Forest (RF) is used to train a model able to classify the fault based on the selected features. The innovative aspect about this method is that, unlike other existing methods, ACO is able to optimize not only the features but also the parameters of the classifier in order to obtain the highest classification accuracy. The proposed method was tested on varying operating condition real dataset consisting of six different gearboxes. In the aim to prove the performance of our method, it had been compared to other conventional methods. The obtained results indicate its robustness, and its accuracy stability to handle the varying operating condition issue in gearboxes fault detection and classification with high efficiency
Module : Automates Programmables Industriels
(Faculté de technologie : Département d’Ingénierie des Systèmes Electriques, 2024) Ikhlef, Boualem
De nos jours le monde industriel utilise de plus en plus des systèmes automatisés dans le but d'améliorer la productivité, la sécurité du personnel, minimiser les couts de maintenance, notamment en installant de la maintenance préventive, faciliter les procédures de maintenance, améliorer la qualité de la production, bien gérer le stock de produit ou de pièces de rechanges...etc.