Abstract / truncated to 115 words (read the full abstract)

This doctoral thesis treats data-driven methodologies to enhance the performance of anomaly detection and virtualization in the context of engine control. Anomalies, specifically knocking combustion pose the major obstacle towards optimal thermal efficiency of an engine and considerable risk of critical damages, thus their detection assumes a priority role for meeting future regulatory targets for engine performance and lifetime. Virtualization on the other hand offers the potential to replace expensive, invasive or inaccessible sensors with mathematical model which adequately approximate these signals. Throughout the thesis, concepts to tackle both of these challenges are presented, with a special emphasis on those incorporating Convolutional Neural Networks (CNN), which in their nature as deep learning approaches offer extensive ... toggle 8 keywords

machine learning – deep learning – engine control – in-cylinder pressure – knocking – virtual sensing – anomaly detection – data-driven

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