This paper introduces a method for the detection of knock occurrences in an internal combustion engine (ICE) using a 1D convolutional neural network trained on in-cylinder pressure data. The model architecture was based on considerations regarding the expected frequency characteristics of knocking combustion. To aid the feature extraction, all cycles were reduced to 60{\deg} 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 and 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 multi-class 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 MAPO (Maximum Amplitude of Pressure Oscillation) detection method, as well as other references reconstructed from previous works. Our analysis indicates that the neural network learned physically meaningful features connected to engine-characteristic resonance frequencies, 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 (on CPU) to classify individual cycles, effectively making it suitable for real-time engine control.
翻译:本文引入了一种在内部燃烧引擎(ICE)中检测敲门情况的方法, 使用一个1D 受内气瓶压力数据培训的进化神经网络 。 模型结构基于对敲门和不敲门周期的预期频率特性的考虑。 为了帮助提取特性, 所有周期都降为 CA 长窗口, 没有进一步处理压力痕迹。 神经网络完全用来自人类专家提供的多种条件和标签的内燃机压力痕迹来进行训练。 最佳模型结构在10倍交叉校准中, 在所有测试组中, 精确度超过92% 。 模型结构基于对敲门和非敲门周期的考虑。 在多级问题中, 每个周期都被评为敲门的专家数量标记为60\deg} CA长窗口。 而90%的周期大多在地面真相中分类。 因此, 最先进的模型( 压力电流动电动电动电动电动) 检测方法大大超出广泛应用的 MAPO (MAPO (MAximum Aclation) 正确度) 的检测方法, 以及从以往工程中重建的其他引用的不交叉校正轨训练周期 。 因此, 我们的机的运行到常规分析显示中, 学习中, 学习的运行中, 显示的机动的机动的机能到普通的轨迹到普通操作方法。