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Field
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, increasing costs and resource waste. This Ph.D. project aims to address these challenges by advancing fault diagnosis and prognosis (FDP) for complex mechatronic systems. Building on Supervisor Team's
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radiation-induced faults. This project aims to develop an optimised and fault-tolerant version of Falcon for such environments. We will simplify complex operations, speed up critical steps using custom
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PhD studentship: Advancing Net-Zero Manufacturing: Physics-Informed Digital Twin for Real-Time Fault Diagnosis and Prognosis in Complex Mechatronic Systems Award Summary 100% fees covered, and a
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emission analysis, wear and oil analysis,…,etc. have been used, through condition monitoring of the rotating machinery, to diagnose and prognosis different faults such as, bearing, crack shaft, gearbox
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of the Paralana Fault Zone in South Australia’s Flinders Ranges, these field-based PhD projects will address the following research questions: What are the relative contributions of radiolysis and serpentinization
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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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representing curved surfaces with piecewise linear approximations. The error introduced by using FE is particularly limiting when modelling dynamic events, as numerical dispersion and dissipation error of waves
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flow models, providing a reliable and efficient approach to designing photobioreactors with reduced trial-and-error efforts while increasing confidence in their performance. By optimising microalgae
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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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magnification and error accumulation by closing the feedback loop of conventional 3DP systems. The candidate will test the developed technologies across multiple application scenarios, including large-scale