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- Title
Sound-Based Abnormal Combustion Classification Model for High Compression Ratio, Spark-Ignition Engines Using Mel-Frequency Cepstrum Coefficients and Ensemble Learning Algorithms.
- Authors
Kim, Seongsu; Kim, Junghwan
- Abstract
A supervised machine learning model was developed to determine knocking in a spark ignition engine. The engine sound was delivered to an operator via an extended metal tube installed on the cylinder block. The sound was recorded using a smartphone at a sample frequency of 48,000 Hz. Thirty-nine features were extracted from the mel-frequency cepstrum and spectral analysis. Neighborhood component analysis was performed to select eight features with the highest contributions. The gentle adaptive boosting scheme, available in MATLAB, achieved the best results among the nine ensemble algorithms used in this study, regardless of whether it was trained using all 39 features or the eight selected features. The best model exhibited 99.98 % accuracy in classifying knock sounds and 99.85 % in classifying normal sounds. A second round of validation was performed to investigate the robustness of the proposed model. The dataset used in this round was acquired from a slightly advanced spark timing case, in which knock intensity varied from mild to severe. The model achieved 100 % accuracy in detecting both knock and normal sounds. Each signal segment contained an individual cycle sound to evaluate the feasibility of a model for detecting individual knock cycles during real-time engine sound monitoring.
- Subjects
SPARK ignition engines; MACHINE learning; SUPERVISED learning; FEATURE extraction; TRANSMISSION of sound; COMBUSTION; BOOSTING algorithms; KNOCK in automobile engines
- Publication
International Journal of Automotive Technology, 2023, Vol 24, Issue 3, p873
- ISSN
1229-9138
- Publication type
Article
- DOI
10.1007/s12239-023-0071-0