Génie Mécaniques
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Item Electrical systems faults diagnosis based on thermography and machine learning techniques(Université M'Hamed Bougara Boumerdès : Faculté de Technologie, 2025) Mahami, Amine; Benazzouz, Djamel(Directeur de thèse)The goal of using AI-driven conditional monitoring in electrical devices is to monitor and trace the beginning and development of deterioration prior to a failure. This degradation eventually results in a system malfunction that impacts the availability of the whole system. Early identification allows for a planned shutdown, averting catastrophic failure and guaranteeing more cost-effective and dependable operation. This study is divided into two major parts: the first part deals with the identification and categorization of faults in induction motors, and the second part deals with the detection and classification of faults in transformers. In machine health management, condition monitoring and problem diagnostics of electrical machines are important study areas. Using infrared thermography method (IRT), a new noncontact and nonintrusive experimental framework is used in the first portion of this thesis to monitor and diagnose defects in a three-phase induction motor. Using IRT to obtain a thermograph of the target machine is the first step in the process. The Speeded-Up Robust Features (SURF) detector and descriptor are then used to extract fault features from the IRT images using the bag-of-visual-word (BoVW) technique. Then, a group learning method known as Extremely Randomized Tree (ERT) is applied to automatically detect different types of induction motor defect patterns. Based on experimental IRT images, the efficacy of the suggested method is evaluated, showcasing its potential as a potent diagnostic tool with superior classification accuracy and stability over alternative approaches. The second part of the thesis presents an experimental framework that uses infrared thermography (IRT) to monitor and diagnose transformer defects in a non-intrusive and non-contact manner. Using IRT to obtain a thermograph of the intended machine is the first step in the process. GIST features are then taken from the database's reference image and every other image. Finally, a machine learning technique known as Support Vector Machine (SVM) is used to automatically identify different fault patterns in the transformer. Based on experimental IRT images and diagnostic results, the efficacy and capacity of the proposed method are assessed, demonstrating its potential as a potent diagnostic tool with high classification accuracy and stability. This method improves operational reliability by facilitating the early identification and detection of transformer failuresItem Analysis of bearings defaults using machine learning techniques(Université M'Hamed Bougara Boumerdès : Faculté de Technologie, 2025) Moussaoui, Imane; Benazzouz, Djamel(Directeur de thèse)Rotating machines have become ubiquitous in contemporary industries, playing a pivotal role in various applications. The consequences of defects in these machines extend beyond mere technical issues, potentially leading to substantial economic losses and posing a significant threat to human safety. Operators often grapple with the intricacies of troubleshooting these complex systems, where a single mistake can have catastrophic consequences. One of the most critical elements in these machines is the bearings. Consequently, numerous researchers have dedicated their time and efforts to addressing this matter. While extensive studies have been conducted in this field, a common limitation is the focus on constant-speed scenarios. In reality, rotating machines typically operate under non-stationary conditions, making constant-speed techniques largely theoretical. This thesis is divided into two essential parts. The first part addresses the challenges of diagnostic resolution under time-varying conditions. Given the dynamic nature of the working environment, understanding and mitigating faults in non-stationary conditions is imperative for practical applications. Our method aims to tackle the diagnostic issue under time-varying conditions. The technique was tested on a bearing database collected under time-varying conditions, containing three types of faults. Vibrational signals are initially processed using the Empirical Wavelet Transform (EWT) to extract AM-FM modes. Subsequently, a list of features is extracted from these modes. For feature selection, the Clan-Based Cultural Algorithm (CCA) is employed, and model training utilizes the Random Forest algorithm. The results demonstrate the robustness of the diagnostic process despite varying conditions. The second part focuses on feature selection, which plays a crucial role in controlling the quality of the diagnostic system and reducing misleading factors. This area of research is increasingly attracting attention, with numerous methods developed. However, many of these techniques require in-depth domain knowledge, particularly concerning parameter tuning and result interpretation. In this work, we introduce a robust technique based on standard deviation and Random Forest methods for sequential feature selection. The method was tested on three different bearing databases, including time-varying conditions, and three signal decomposition techniques (EWT, EMD, and MODWPT). It provided promising results in terms of both quality and quantity, being user-friendly and not demanding extensive knowledge in the optimization fieldItem Energy-aware USVs path planning(Université M'Hamed Bougara : Faculté de Technologie, 2022) Ouelmokhtar, Hand; Benazzouz, Djamel(Directeur de thèse)Unmanned Surface Vehicles (USV) are an innovative solution for various maritime applications such as marine navigation, rescue, environmental monitoring and surveillance, etc. USVs offer the advantage to operate in hostile or dangerous environments where humans cannot safely or not at all perform. In general, USVs operate in harsh environmental conditions that require accuracy, reliability and autonomy. To meet these critical requirements, the focus on USVs and their applications is gradually performed. One of the most important problems to be solved is that of trajectory planning. In order to execute the planned tasks, the USVs must operate in an autonomous way and manage their resources optimally in order to minimize human interventions. Thus, performance and autonomy criteria are very important to consider when executing any type of task. In this thesis, we address the general problem of maritime surveillance using a USV equipped with an on-board LiDAR (Light Detection and Ranging) that allows remote coverage of distant points. The objectives are to cover the maximum area with lowest energy cost while avoiding collisions with obstacles. To solve this problem, we used two optimization approaches: • The first one consists in using heuristic methods based on multi-objective evolutionary algorithms. In this case, two algorithms are used and compared. One consists of a local search method known as Pareto Archived Evolution Strategy (PAES). Other consists of a population-based search algorithm called Non-Dominated Genetic Sorting Algorithm II (NSGA-II). • A novel method is proposed to improve the performance of evolutionary algorithms when solving path planning problems by reducing the size of chromosomes. • The second approach isbased on the exact method using a Mixed Integer Programming (MIP) model with two objective functions inspired by both the Covering Salesman Problem (CSP) and the Travelling Salesman Problem with Profit (TSPP).Item Contribution au diagnostic des systèmes par bond graph(Université M'Hamed Bougara : Faculté de Technologie, 2021) Termeche, Adel; Benazzouz, Djamel(Directeur de thèse)La détection et l'isolation des défauts (FDI) est une tâche essentielle qui permet d’éviter les conséquences des pannes sur les performances du système. Le Bond Graph, en tant qu'outil de modélisation, fournit à travers ses propriétés structurelles et causales, une génération automatique de Relations de Redondance Analytique (RRA). Ces relations sont utilisées pour les applications de diagnostic, elles constituent les contraintes mathématiques qui permettent de vérifier la cohérence entre les mesures du processus et le comportement du système de référence représenté par le modèle. L'approche de diagnostic RRA classique permet à la fois de détecter et d'isoler le composant défectueux du système. Dans ce travail, l'objectif principal est d'augmenter le nombre de défauts isolables en augmentant le nombre de RRA, en utilisant la sortie du modèle de graphe de liaison avec la sortie mesurée du système réel. La représentation Bond Graph sous forme de transformation linéaire fractionnelle (LFT) a été exploitée pour l’intégration de la fonction linéaire, cette dernière est utilisée pour l’amélioration de la détection des défauts en présence des erreurs de mesures. L'intérêt innovant de ce travail est que le nombre des défauts isolés peut être amélioré sans l'ajout de capteurs supplémentaires. Suite à la discussion générale de la méthode proposée, un sous-système robotique (traction d'un robot mobile omnidirectionnel) est envisagé pour valider la procédure proposée. Deux scénarii défectueux sont ensuite présentés et discutés en utilisant à la fois l'approche classique et l'approche proposée. La méthode proposée est capable d'isoler 3 défauts qui ne peuvent pas être isolées avec l’approche de RRA classique.