Theses
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Machine Learning for Real-Time Temperature Monitoring of Electric Machines
Ryans thesis investigates the application of machine learning techniques to the real-time thermal modelling of electric machines. Traditional methods like high-order lumped parameter models and finite element analysis, while effective, are computationally expensive and often unsuitable for real-time applications. This work seeks to address these limitations by developing data-driven machine learning-based models that are fast, easy to train, generalisable, and accurate.