Abstract
Engine knocking is often identified by an operator based on the combustion sound emanating from the cylinder block during engine calibration in a test cell. Such human-hearing-based acoustic knock determination is considered the most reliable real-time knock-monitoring method, and it is necessary for selecting an accurate spark timing. In this study, the characteristics of this combustion sound were investigated. To this end, a normal engine calibration experiment was conducted. The engine sound was recorded under various operating conditions by using a copper tube attached to the cylinder block and the microphone of a smartphone. The measured signals were subjected to acoustic analyses, including Fast Fourier transform, smoothing, spectral analysis, and autocorrelation, to compare the sounds recorded at the knock borderline with those recorded in the cases with significantly advanced spark timing. The results of these analyses revealed several features that distinguished the knock sound from the normal combustion sound. The knock sound exhibited distinctions in a high-frequency band between 6,000 and 8,000 Hz. These features can be used to develop effective knock classification models.
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Abbreviations
- KBL:
-
knock borderline
- ECU:
-
engine control unit
- BMEP:
-
brake mean effective pressure
- CA:
-
crank angle
- aTDC:
-
after top-dead center
- RMS:
-
root mean square
- FFT:
-
fast fourier transform
- ACF:
-
auto-correlation function
References
Abu-Qudais, M. (1996). Exhaust gas temperature for knock detection and control in spark ignition engine. Energy Conversion and Management 37, 9, 1383–1392.
Bennett, C., Dunne, J. F., Trimby, S. and Richardson, D. (2017). Engine cylinder pressure reconstruction using crank kinematics and recurrently-trained neural networks. Mechanical Systems and Signal Processing, 85, 126–145.
Brunt, M. F., Pond, C. R. and Biundo, J. (1998). Gasoline engine knock analysis using cylinder pressure data. SAE Trans., 1399–1412.
Cho, S., Song, C., Oh, S., Min, K., Ha, K. P. and Kim, B. S. (2018). An experimental study on the knock mitigation effect of coolant and thermal boundary temperatures in spark ignited engines. SAE Paper No. 2018-01-0213.
Ettefagh, M. M., Sadeghi, M. H., Pirouzpanah, V. and Tash, H. A. (2008). Knock detection in spark ignition engines by vibration analysis of cylinder block: A parametric modeling approach. Mechanical Systems and Signal Processing 22, 6, 1495–1514.
Glowacz, A. (2019). Acoustic fault analysis of three commutator motors. Mechanical Systems and Signal Processing, 133, 106226.
Gryllias, K. C. and Antoniadis, I. A. (2013). Estimation of the instantaneous rotation speed using complex shifted Morlet wavelets. Mechanical Systems and Signal Processing 38, 1, 78–95.
Hamilton, L. J. and Cowart, J. S. (2008). The first wide-open throttle engine cycle: Transition into knock experiments with fast in-cylinder sampling. Int. J. Engine Research 9, 2, 97–109.
Kalghatgi, G. (2018). Knock onset, knock intensity, superknock and preignition in spark ignition engines. Int. J. Engine Research 19, 1, 7–20.
Lee, J. H., Hwang, S. H., Lim, J. S., Jeon, D. C. and Cho, Y. S. (1998). A new knock-detection method using cylinder pressure, block vibration and sound pressure signals from a SI engine. SAE Trans., 1808–1819.
Leppard, W. R. (1982). Individual-cylinder knock occurence and intensity in multicylinder engines. SAE Paper No. 820074.
Liu, H., Wang, Z., Qi, Y., He, X., Wang, Y. and Wang, J. (2019). Experiment and simulation research on super-knock suppression for highly turbocharged gasoline engines using the fuel of methane. Energy, 182, 511–519.
Liu, X., Randall, R. B. and Antoni, J. (2008). Blind separation of internal combustion engine vibration signals by a deflation method. Mechanical Systems and Signal Processing 22, 5, 1082–1091.
Maurya, R. K., Pal, D. D. and Agarwal, A. K. (2013). Digital signal processing of cylinder pressure data for combustion diagnostics of HCCI engine. Mechanical Systems and Signal Processing 36, 1, 95–109.
Nikula, R. P., Karioja, K., Pylvänäinen, M. and Leiviskä, K. (2020). Automation of low-speed bearing fault diagnosis based on autocorrelation of time domain features. Mechanical Systems and Signal Processing, 138, 106572.
Novella, R., Pla, B., Bares, P. and Jiménez, I. (2022). Acoustic characterization of combustion chambers in reciprocating engines: An application for low knocking cycles recognition. Int. J. Engine Research 23, 1, 120–131.
Pachaud, C., Salvetat, R. and Fray, C. (1997). Crest factor and kurtosis contributions to identify defects inducing periodical impulsive forces. Mechanical Systems and Signal Processing 11, 6, 903–916.
Payri, F., Luján, J. M., Martín, J. and Abbad, A. (2010). Digital signal processing of in-cylinder pressure for combustion diagnosis of internal combustion engines. Mechanical Systems and Signal Processing 24, 6, 1767–1784.
Peyton Jones, J. C., Shayestehmanesh, S. and Frey, J. (2020). Parametric modelling of knock intensity data using a dual log-normal model. Int. J. Engine Research 21, 6, 1026–1036.
Peyton Jones, J. C., Spelina, J. M. and Frey, J. (2014). Optimizing knock thresholds for improved knock control. Int. J. Engine Research 15, 1, 123–132.
Pichler, K., Lughofer, E., Pichler, M., Buchegger, T., Klement, E. P. and Huschenbett, M. (2016). Fault detection in reciprocating compressor valves under varying load conditions. Mechanical Systems and Signal Processing, 70, 104–119.
PowerLink (2020). Eddy current dynamometer working principle, https://www.powerlinkpt.com/eddy-current-dynamometer-en/
Rafiee, J. and Tse, P. W. (2009). Use of autocorrelation of wavelet coefficients for fault diagnosis. Mechanical Systems and Signal Processing 23, 5, 1554–1572.
Su, W., Wang, F., Zhu, H., Zhang, Z. and Guo, Z. (2010). Rolling element bearing faults diagnosis based on optimal Morlet wavelet filter and autocorrelation enhancement. Mechanical Systems and Signal Processing 24, 5, 1458–1472.
Wang, Z., Liu, H. and Reitz, R. D. (2017). Knocking combustion in spark-ignition engines. Progress in Energy and Combustion Science, 61, 78–112.
Wodecki, J., Kruczek, P., Bartkowiak, A., Zimroz, R. and Wyłomańska, A. (2019). Novel method of informative frequency band selection for vibration signal using Nonnegative Matrix Factorization of spectrogram matrix. Mechanical Systems and Signal Processing, 130, 585–596.
Xu, J., Feng, Y., Chang, S. and Guo, T. (2020). Numerical and experimental study on knock sources in spark ignition engine with electromagnetic valve train. Int. J. Automotive Technology 21, 6, 1369–1378.
Yang, Z., Yan, W., Jin, L., Li, F. and Hou, Z. (2020). A novel feature representation method based on original waveforms for acoustic emission signals. Mechanical Systems and Signal Processing, 135, 106365.
Yun, D. U. and Lee, S. K. (2017). Objective evaluation of the knocking sound of a diesel engine considering the temporal and frequency masking effect simultaneously. J. Sound and Vibration, 397, 282–297.
Acknowledgement
This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korean government (MSIT) (No. 2019R1F1A1056540). Author would like to express special thanks to Korea Institute of Machinery and Materials and Hyundai Motors Company for technical supports.
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Kim, J. Acoustic Characteristics of the Abnormal Combustion in a High-Compression Ratio, Spark-Ignition Engine. Int.J Automot. Technol. 23, 1185–1195 (2022). https://doi.org/10.1007/s12239-022-0105-z
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DOI: https://doi.org/10.1007/s12239-022-0105-z