Knock Detection in Combustion Engine Time Series Using a Theory-Guided 1-D Convolutional Neural Network Approach

This article introduces a method for the detection of knock occurrences in an internal combustion engine (ICE) using a 1-D convolutional neural network trained on in-cylinder pressure data. The model architecture is based on expected frequency characteristics of knocking combustion. All cycles were reduced to
$60^{\circ }$
CA long windows with no further processing applied to the pressure traces. The neural networks were trained exclusively on in-cylinder pressure traces from multiple conditions, with labels provided by human experts. The best-performing model architecture achieves an accuracy of above 92% on all test sets in a tenfold cross-validation when distinguishing between knocking and non-knocking cycles. In a multiclass problem where each cycle was labeled by the number of experts who rated it as knocking, 78% of cycles were labeled perfectly, while 90% of cycles were classified at most one class from ground truth. They thus considerably outperform the broadly applied maximum amplitude of pressure oscillation (MAPO) detection method, as well as references reconstructed from previous works. Our analysis indicates that the neural network learned physically meaningful features connected to engine-characteristic resonances, thus verifying the intended theory-guided data science approach. Deeper performance investigation further shows remarkable generalization ability to unseen operating points. In addition, the model proved to classify knocking cycles in unseen engines with increased accuracy of 89% after adapting to their features via training on a small number of exclusively non-knocking cycles. The algorithm takes below 1 ms to classify individual cycles, effectively making it suitable for real-time engine control.