Génie Mécaniques
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Item Mechatronic systems diagnostics using signal processing based techniques(Université M'Hamed Bougara Boumerdès : Faculté de Technologie, 2026) Damou, Ali; Benazzouz, Djamel(Directeur de thèse); Benazzouz, DjamelIndustry 4.0, or the factory of the future, is advancing faster than ever, with intensive competition and a more challenging industry. In this competitive context, rotating machinery, particularly gearbox systems, plays a crucial role in transmitting power and altering speed and torque; they are essential for many industrial processes. As one of the key mechanical parts in gearbox, bearings are often the heart of a wide range of industrial mechanisms, they are regarded as essential elements in various applications, including wind turbines, helicopters, robots and aerospace systems. Consequently, a sudden malfunction can significantly impair system performance and may even result in catastrophic failure or total breakdown. Ensuring their reliable operation through efficient maintenance and fault detection strategies has become essential to prevent unexpected failures and system downtime. Predictive maintenance strategies based on condition monitoring have garnered substantial attention in recent years. Condition-Based Maintenance (CBM) plays a pivotal role in maintaining optimal system performance by predicting failures and minimizing unnecessary preventive interventions, thus reducing maintenance costs. Various CBM techniques have been developed to assess gearbox health, including acoustic emission, thermal monitoring, chemical analysis, current signature analysis, and vibration analysis. Among these, vibration-based condition monitoring has received particular focus, as the vibration signals generated by gearboxes contain valuable diagnostic information about their health state. This dissertation focuses on bearing fault diagnosis in gearbox systems, emphasizing the importance of predictive maintenance and early fault detection. However, these signals are inherently non-stationary, with defect signatures often masked by noise and irrelevant components, especially in early-stage faults. Moreover, the presence of strong non-Gaussian noise often conceals fault frequencies in the spectrum. Additionally, when multiple bearings within the system share identical dimensions, they exhibit similar characteristic fault frequencies. In such cases, even ifafaultfrequencyis detected in the vibration spectrum, it becomes challenging to identify which specific bearing is the source of the fault, complicating fault localization. This thesis also deals with this issue by developing a new automatic approach to detect, identify, and classify several bearing defects. The intelligent method is a combination of Wavelet Packet Transform (WPT) and machine learning techniques. WPT was employed to decompose vibration signals into discrete frequency bands; relevant features were extracted from each sub-band in the time domain, enabling the capturing of distinct fault characteristics across various frequency ranges sensitive to different fault conditions. These features served as inputs to machine learning classifiers, facilitating the accurate identification and classification of defective bearings. A dynamic simulation of a single stage spur gearbox was employed to generate vibration data under both healthy conditions and three distinct bearing defects, demonstrating the capability of the proposed approach to accurately detect and identify bearing defects across all casesItem 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 failures