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 cases