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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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control system that enhances Annual Energy Production (AEP), reduces mechanical stress, and improves fault detection using machine learning (ML) and physics-based modelling. The candidate will gain hands
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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
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suite of specialised facilities: UAV Fuel Rig with Five Degradation Faults: Simulates various degradation scenarios in unmanned aerial vehicle (UAV) fuel systems, enabling research into fault detection
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Degradation Faults: Simulates various degradation scenarios in unmanned aerial vehicle (UAV) fuel systems, enabling research into fault detection, isolation, and prognostics. Machine Fault Simulator
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research into fault detection, isolation, and prognostics. Machine Fault Simulator for Rotating Machinery Faults: A versatile platform that replicates common faults in rotating machinery, such as imbalance
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hardware design, and build in fault detection and correction to ensure secure, efficient operation in space systems. The outcome will be a high-performance, fault-tolerant Falcon implementation, enhancing
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using thermographic Non-Destructive Testing (NDT), a critical method for ensuring aircraft safety and reliability. NDT is increasingly vital in the aviation sector, enabling the detection of hidden
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of the challenges is fault detection and diagnosis of bearings subject to low (rotational) speed. As vibration/acoustic signals generated by the faults of low-speed bearings are very weak and often covered by strong
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AI-Driven Digital Twin for Predictive Maintenance in Aerospace - In Partnership with Rolls-Royce PhD
generate vast amounts of operational and maintenance data, much of it remains fragmented and underutilized. Unlocking insights from this unstructured data could enable earlier fault detection, improved