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AI-Driven Digital Twin for Predictive Maintenance in Aerospace – In Partnership with Rolls-Royce PhD
engineers detect faults earlier, track system degradation, and make better-informed maintenance decisions. But how can we turn this complex information into something reliable, explainable, and actionable
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of artificial intelligence (AI) nowadays, it has become possible to develop a fast-response AI-based condition monitoring system for gas turbine engines. The objective of the project is to develop novel AI-based
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substrate, enabling the layer-by-layer construction of complex 3D objects. The temperature field created by the interaction between the electric arc and the material is a critical factor influencing the
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involves feeding a metal filler wire, either coaxially or off-axis, into an electric arc to create a molten pool that solidifies on a substrate, enabling the layer-by-layer construction of 3D objects
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lower orbit space debris). The increasing density of space objects in Lower Earth Orbit (LEO), including the proliferation of satellite constellations, further exacerbates the risk of collisions and the
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-driving cars. This PhD research will be supervised by Professor Daniel J. Auger and Dr Abbas Fotouhi . The centre has a strong track record in battery systems research; members have pioneered state
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publication-oriented, allowing you to build a strong academic and professional track record in energy-efficient and secure AI hardware innovation. By the end of this PhD, candidates will possess advanced
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managed to allow for efficient solar energy harvesting. This project will deliver novel methods for modelling and controlling LGS structural dynamics in the extreme orbital environment. The objectives
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of their machines is maximised, or machine downtime is minimised. The aim is to develop a smart sensor prototype and demonstrator for condition monitoring of low-speed bearings. The following objectives are defined
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to industrial clients such as Boeing, BAE Systems, Rolls-Royce, Meggitt, Thales, MOD, Bombardier, QinetiQ, Thales, Network Rail, Schlumberger and Alstom. Entry requirements A minimum of a 2:1 first degree in a