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maintenance. Development of machine/deep learning methods to detect fault, provide early warning and reporting, and forecast lifetime trend of batteries, to support predictive maintenance and improve energy
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, curved element optimisation, multiscale geometric feature detection, and error-controlled surface approximation. The resulting technology will be tested on real industrial geometries provided by partners
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-agent reinforcement learning (MARL) framework for cyber-physical networked fault-tolerant control of renewable energy-fed smart grids under adversarial conditions [6]-[9]. Multiple autonomous agents will
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feature detection, and error-controlled surface approximation. The resulting technology will be tested on real industrial geometries provided by partners in aerospace and energy such as Airbus and UKAES
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Aerial Vehicles (UAV), e.g. drones, are increasingly used for equipment anomaly and fault detection in offshore wind turbines. When the drones are employed to take images, the quality of the images can be
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provide large and complex datasets. By applying advanced pattern recognition and clustering algorithms, the aim is to automatically detect coherent spatial domains. These domains represent regions with
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This PhD at Cranfield University explores the development of resilient, AI-enabled electronic systems capable of detecting faults and autonomously recovering from failures in real time. The project
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-driven shifts in species distributions. Currently, barnacles and other species are manually counted from over 3,000 images each year, which is time-consuming and prone to human error. This project will
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of Robot Behaviours in Simulation: Develop methods to automatically generate diverse test scenarios in a virtual environment to efficiently find faults in robotic skills. This involves using intelligent
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Fuel Rig with Five Degradation Faults: Simulates various degradation scenarios in unmanned aerial vehicle (UAV) fuel systems, enabling research into fault detection, isolation, and prognostics. Machine