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was conceived, designed and built entirely in-house with an open architecture, offering maximum flexibility for the integration of new functionalities. Over the years, it has been systematically improved with
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exploring various architectures and unsupervised learning techniques to identify anomalies and diagnose specific fault types based on processed sensor data (e.g., vibrations, currents). Edge device deployment
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operational employment. This doctoral research will thus leverage the power of graph neural networks – a novel ML architecture, capable of learning fundamental physical behaviour by modelling systems as graphs
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