A Precise and Reliable Engine Knock Detection Utilizing Meta Classifier

An increase in temperature and pressure can cause spontaneous ignition of the air-fuel mixture in internal combustion engines, reducing engine efficiency, lifespan, and increasing air pollution. Typically, to predict and detect this effect, a knock sensor is used, which has a low detection accuracy due to the engine vibration noise. In this work, a machine learning model based on a meta-classifier is proposed and implemented for real-time fault detection in combustion engines. First, actual knock sensor data are recorded at diverse engine speeds from our engine test bench. The local dataset is preprocessed and scaled. Then, 30 different features in the time and frequency domains are investigated. Dimensionality of data is reduced employing recursive feature elimination. Then, a stacking classifier is utilized to address the classification problem by combining several classification models through the use of a metaclassifier. To enhance the assessment of the experimental outcomes in knock detection, k-fold cross-validation is utilized to gauge the model's performance with new data. The result shows the proposed method has around 12% higher accuracy during 5 cross folds with least amount of variation. Finally, the model is implemented on an ARM MCU and showed an execution time of 8.9ms, which validates its reliability for real-time operation.