Knock Detection with Ion Current and Vibration Sensor: A Comparative Study of Logistic Regression and Neural Networks

Abstract

Knock detection is critical for maintaining engine performance and preventing damage in spark-ignition engines. This study explores the use of ion current and knock indicators derived from a vibration sensor (KIv) and ion current (KIi) to improve knock detection accuracy. Traditional threshold-based methods rely on KIv, but they are susceptible to mechanical noise and cylinder variations. In this work, we applied both logistic regression and neural networks, including fully connected (FCNN) and convolutional neural networks (CNN), to classify knock events based on these indicators. The CNN models used ion current as the primary input, with an extended version incorporating both KIv and KIi into the fully connected layers. The models were evaluated using area under the curve (AUC) as the primary performance metric. The results show that the CNN model with additional inputs outperformed the other models, achieving a better and more consistent performance across cylinders. The dual-input logistic regression and CNN models demonstrated reduced cylinder-to-cylinder variation in classification performance, providing a more consistent knock detection accuracy across all cylinders. These findings suggest that combining ion current and knock indicators enhances knock detection reliability, offering a robust solution for real-time applications in engine control systems.

Keywords

• knock detection
• machine learning
• ion current