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Field
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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
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on the feasibility for using neutrons to image high density objects. Neutrons have higher penetrability than X-rays but neutron generation and imaging solutions are more challenging and so they are yet to be fully
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abort (or not engage) if the bright white lines that fit a defined and rigid expectation are not clearly visible. These systems use algorithms, rather than AI machine learning, to detect road markings and
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innovation and find diverse applications across industries such as aerospace, energy, and automotive. Among its various techniques, wire-arc directed energy deposition (WA-DED) stands out as a highly promising
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optimization of batteries against the swelling phenomenon. This project aims at developing scientific machine learning approaches based on the Bayesian paradigm and electrochemical-thermomechanical models in
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mechanics, such as inferring the instantaneous state of a fluid’s velocity field from sensors embedded in its boundary. The research objective is to find the best way to embed simple partial differential
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environments—such as fleets with multiple aircraft types. Objectives Objective 1: Map current data types, structures, and interoperability challenges to build a detailed "as-is" understanding of current
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resilient to future change. This project will evaluate potential future land use configurations in several countries, exploring where and how biomass production can support multiple objectives for a
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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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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