Engine knock determination has been conducted in various ways for spark timing calibration. In the present study, a knock classification model was developed using a machine learning algorithm. Wavelet packet decomposition (WPD) and ensemble empirical mode decomposition (EEMD) were employed for the characterization of the in-cylinder pressure signals from the experimental engine. The WPD was used to calculate 255 features from seven decomposition levels. EEMD provided total 70 features from their intrinsic mode functions (IMF). The experimental engine was operated at advanced spark timings to induce knocking under various engine speeds and load conditions. Three knock intensity metrics were employed to determine that the dataset included 4158 knock cycles out of a total of 66,000 cycles. The classification model trained with 66,000 cycles achieved an accuracy of 99.26% accuracy in the knock cycle detection. The neighborhood component analysis revealed that seven features contributed significantly to the classification. The classification model retrained with the seven significant features achieved an accuracy of 99.02%. Although the misclassification rate increased in the normal cycle detection, the feature selection decreased the model size from 253 to 8.25 MB. Finally, the compact classification model achieved an accuracy of 99.95% with the second dataset obtained at the knock borderline (KBL) timings, which validates that the model is sufficient for the KBL timing determination.
A mechanical verification of the stressing algorithm for negative cost cycle detection in networks
