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mechanical wear and electronic errors pose risks to efficiency, increasing costs and resource waste. This Ph.D. project aims to address these challenges by advancing fault diagnosis and prognosis (FDP
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This PhD project will focus on developing, evaluating, and demonstrating an intelligent solution of diagnosis and prognosis for rotating machinery to enhance safety, reliability, maintainability and
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& Statistics Project description Unmanned Aerial Vehicle (UAV) e.g., drones are increasingly used for equipment anomaly and fault detection. When the drones are employed to take images, the quality of the images
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of smart CFRP composites under varying EOCs. Enhance fault detection and diagnosis for delamination and Barely Visible Impact Damage (BVID), aiming for high diagnostic performance and low false alarm rates
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thematic areas: Control systems, computational intelligence and machine learning, autonomous systems, optimization and networks, embedded and real-time systems hardware and software, fault diagnosis, cyber
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profile: • Very good degree (Master or Diploma) in aerospace engineering, mechanical engineering, computer science or a comparable field. • Experience in machine elements, structural analysis, fault