Département de Construction Aeronautique http://localhost:8080/xmlui/handle/123456789/87 2026-10-14T05:01:43Z 2026-10-14T05:01:43Z Benabdelmola, Nesrine SARI, Elkahina (Promotrice) AISSAOUI, Abdelkadir ( Co-Promoteur) SEHAB, Nadjette (Co-Promotrice) http://localhost:8080/xmlui/handle/123456789/41459 2026-10-07T10:16:38Z 2026-01-01T00:00:00Z

Surveillance et Diagnostic des Machines Tournantes par Analyse Vibratoire Assistée par Intelligence Artificielle Benabdelmola, Nesrine; SARI, Elkahina (Promotrice); AISSAOUI, Abdelkadir ( Co-Promoteur); SEHAB, Nadjette (Co-Promotrice) L’objectif de ce projet de fin d’études est d’établir un modèle hybride d’apprentissage profond CNN-LSTM pour le diagnostic des défauts dans les machines tournantes en utilisant l’analyse vibratoire avec une implémentation MATLAB. L’ensemble des signaux utilisé pour l’apprentissage a été fourni par la base de données de l’Université de Case Western Reserve (CWRU). Dans ce contecste afin de valider nos résultats numériques, un band d’essai et une chaine d’acquisition des données ont été réalisés au sein du laboratoire de vibration au centre de recherche de développent militaire (CRD) pour déterminer les paramètres vibratoires de plusieurs défauts comme le défaut de roulement, d’engrenage, désalignement et le déséquilibre. Les résultats obtenus montrent que notre modèle hybride CNN-LSTM est très performant pour faire un bon diagnostic des défauts des machines tournantes qui été validé par les résultats du band d’essai The goal of this final project study is to develop a hybrid CNN-LSTM deep learning model for fault diagnosis in rotating machinery using vibration analysis, implemented in MATLAB. The signal dataset used for training was provided by the Case Western Reserve University (CWRU) bearing data center. In this context, to validate our numerical results, a test rig and a data acquisition chain were set up within the vibration laboratory at the Military Research and Development Center (CRD) to determine the vibration parameters of several faults, such as bearing faults, gear faults, misalignment, and unbalance. The results obtained show that our hybrid CNN-LSTM model is highly effective in accurately diagnosing rotating machinery faults, which was successfully validated by the experimental results from the test rig. ill., Bibliogr.Cote:001/2026-Mémoire de Master option Structure

2026-01-01T00:00:00Z DORBANI, Nahr El Imene ZOUAOUI, Sara AMRI, Redha. ( Promoteur) LAGHA, Mohaned. ( Promoteur) http://localhost:8080/xmlui/handle/123456789/41057 2025-12-01T11:08:25Z 2005-01-01T00:00:00Z

Development Of Flight Controller For Drones By Using MCU DORBANI, Nahr El Imene; ZOUAOUI, Sara; AMRI, Redha. ( Promoteur); LAGHA, Mohaned. ( Promoteur) This study focuses on the development of a flight controller for drones, utilizing both classic PIC microcontrollers and dsPICs to optimize performance. The main objective is to design and implement an embedded system capable of ensuring stable and precise control of a drone in flight. The approach includes the integration of inertial sensors (accelerometers, gyroscopes) through the I²C communication protocol, the implementation of PID control loops, and programming the microcontrollers using MikroC and MikroC Pro for dsPIC. Data acquisition and interaction with sensors were handled via I²C and UART, ensuring accurate and synchronized measurements essential for flight stabilization. Tools such as SerialPlot, Docklight, and Advanced Serial Port Terminal were used for real-time data visualization and analysis during the testing phases. The project combines modeling, simulation, and hardware/software implementation, with successive tests conducted to validate the controller’s stability and responsiveness. The results demonstrate that the developed system effectively stabilizes the drone’s main axes while addressing real-time processing constraints and precise sensor calibration. This study highlights the complementarity between PIC and dsPIC microcontrollers in meeting the demands of complex embedded systems, providing a concrete contribution to the development of compact, reliable, and scalable flight controllers applicable in aeronautics and robotics. ill., Bibliogr. Cote: 021/2025 option Avionique.

2005-01-01T00:00:00Z Khodjet Kesba, Mohamed (enseignant) http://localhost:8080/xmlui/handle/123456789/39743 2025-05-26T09:57:36Z 2025-01-01T00:00:00Z

Course handout Project management Master Avionics S3/M2 Khodjet Kesba, Mohamed (enseignant) Course Objectives: This course is designed for Master's level students specializing in avionics. It aims to equip students with comprehensive knowledge and practical skills in managing complex projects within the aeronautical industry. The objectives are designed to ensure that students understand and can apply key project management concepts and practices relevant to the unique challenges of aeronautical projects. The course will focus on developing the ability to plan, execute, and evaluate projects effectively while addressing industry-specific requirements. 49 p.;ill.

2025-01-01T00:00:00Z Bentrad, Hocine (enseignant) Bentrad, Hocine (enseignant) http://localhost:8080/xmlui/handle/123456789/35945 2024-12-19T11:48:42Z 2024-01-01T00:00:00Z

Technologie des Turboréacteurs Bentrad, Hocine (enseignant); Bentrad, Hocine (enseignant) 107 p.; ill..-La Page de garde porte en plus : Recueille De Cours : Licence (3) Propulsion Avions

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