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Noise Source Identification of a Gasoline Engine Based on Parameters Optimized Variational Mode Decomposition and Robust Independent Component Analysis
Technical Paper
2020-01-0425
ISSN: 0148-7191, e-ISSN: 2688-3627
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English
Abstract
Noise source identification and separation of internal combustion engines is an effective tool for engine NVH (noise, vibration and harshness) development. Among various experimental approaches, noise source identification using signal processing has attracted extensive attention because of that the signal can be easily acquired and the requirements for equipment is relatively low. In this paper, variational mode decomposition (VMD) combined with independent component analysis (ICA) is used for noise source identification of a turbo-charged gasoline engine. Existing algorithms have been proved to be effective to extract signal features but also have disadvantages. One of the key problems in presently used method is that the main components of the signal, i.e. the main source of the noise, are unknown in advance. Thus the parameters selection of signal processing algorithms, which has a significance influence on the identification result, has no uniform criterion. To solve this problem, a parameter selection method using Kurtosis index is developed to optimize the decomposition level and the quadratic penalty of VMD. After the signal is decomposed into several relevant intrinsic mode functions (IMFs), ICA is employed to extract independent signal sources. In addition, continuous wavelet transform is used to analyze the time-frequency characteristics of the ICA results. The combined technique alleviates the problem of parameters selection in VMD and overcomes the problem that the number of sensors must be larger than or equal to the number of separated components in ICA. The advantages of the proposed method are confirmed by experimental study and simulation results. The proposed method can separate the main noise sources of the gasoline engine accurately.
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Yang, X., Bi, F., Zhang, L., Bi, X. et al., "Noise Source Identification of a Gasoline Engine Based on Parameters Optimized Variational Mode Decomposition and Robust Independent Component Analysis," SAE Technical Paper 2020-01-0425, 2020, https://doi.org/10.4271/2020-01-0425.Data Sets - Support Documents
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References
- Shu , G.Q. and Liang , X.Y. Identification of Complex Diesel Engine Noise Sources Based on Coherent Power Spectrum Analysis Mechanical Systems and Signal Processing 21 1 405 416 2007 10.1016/j.ymssp.2006.06.001
- Thomas , A. , Gosain , A. , and Balachandran , P. Vehicular Cabin Noise Source Identification and Optimization Using Beamforming and Acoustical Holography SAE Technical Paper 2014-01-0004 2014 https://doi.org/10.4271/2014-01-0004
- Sakamoto , I. and Tanaka , T. Application of Acoustic Holography to Measurement of Noise on an Operating Vehicle SAE Technical Paper 930199 1993 https://doi.org/10.4271/930199
- Zhang , J.H. and Bing , H. Analysis of Engine Front Noise Using Sound Intensity Techniques Mechanical Systems and Signal Processing 19 1 213 221 2005 10.1016/j.ymssp.2004.03.007
- Lee , S. , Shin , T. , Kim , B. , and Lee , S. Identification of Engine Noise Source based on Acoustic Source Quantification Using Sound Intensity and Particle Velocity Measurement SAE Technical Paper 2013-01-1978 2013 https://doi.org/10.4271/2013-01-1978
- Wang , X. , Bi , F. Liu , C. Du , X. et al. Blind Source Separation and Identification of Internal Combustion Engine Noise Based on Independent Component and Wavelet Analysis 2011 International Conference on Electrical and Control Engineering China 2011
- El Badaoui , M. , Daniere , J. , Guillet , F. , and Serviere , C. Separation of Combustion Noise and Piston-Slap in Diesel Engine - Part I: Separation of Combustion Noise and Piston-Slap in Diesel Engine by Cyclic Wiener Filtering Mechanical Systems and Signal Processing 19 6 1209 1217 2005 10.1016/j.ymssp.2005.08.010
- Serviere , C. , Lacoume , J.L. , and El Badaoui , M. Separation of Combustion Noise and Piston-Slap in Diesel Engine - Part Ii: Separation of Combustion Noise and Piston-Slap Using Blind Source Separation Methods Mechanical Systems and Signal Processing 19 6 1218 1229 2005 10.1016/j.ymssp.2005.08.026
- Bi , F. , Lin , L. , Zhang , J. , and Ma , T. Source Identification of Gasoline Engine Noise Based on Continuous Wavelet Transform and Eemd-Robustica Applied Acoustics 100 34 42 2015 10.1016/j.apacoust
- Bianciardi , F. , Janssens , K. , Gryllias , K. , Delvecchio , S. et al. Assessment of Combustion Mechanical Noise Separation Techniques on a V8 Engine SAE Technical Paper 2017-01-1846 2017 https://doi.org/10.4271/2017-01-1846
- Huang , N.E. , Shen , Z. , Long , S.R. , Wu , M.L.C. et al. The Empirical Mode Decomposition and the Hilbert Spectrum for Nonlinear and Non-Stationary Time Series Analysis Proceedings of the Royal Society a-Mathematical Physical and Engineering Sciences 454 1971 903 995 1998 10.1098/rspa.1998.0193
- Wu , Z.H. and Huang , N.E. A Study of the Characteristics of White Noise Using the Empirical Mode Decomposition Method Proceedings of the Royal Society A-Mathematical Physical and Engineering Sciences 460 2046 1597 1611 2004 10.1098/rspa.2003.1221
- Dragomiretskiy , K. and Zosso , D. Variational Mode Decomposition Ieee Transactions on Signal Processing 62 3 531 544 2014 10.1109/tsp.2013.2288675
- Yao , J.C. , Xiang , Y. , Qian , S. , Wang , C.S. et al. Noise Source Identification of Diesel Engine Based on Variational Mode Decomposition and Robust Independent Component Analysis Applied Acoustics 116 184 194 2017 10.1016/j.apacoust.2016.09.026
- Li , W. , Gu , F. , Ball , A.D. , Leung , A.Y.T. , and Phipps , C.E. A Study of the Noise from Diesel Engines Using the Independent Component Analysis Mechanical Systems and Signal Processing 15 6 1165 1184 2001 10.1006/mssp.2000.1366
- Tang , B. , Dong , S. , and Song , T. Method for Eliminating Mode Mixing of Empirical Mode Decomposition Based on the Revised Blind Source Separation Signal Processing 92 1 248 258 2012 10.1016/j.sigpro.2011.07.013
- Albarbar , A. , Gu , F. , and Ball , A.D. Diesel Engine Fuel Injection Monitoring Using Acoustic Measurements and Independent Component Analysis Measurement 43 10 1376 1386 2010 10.1016/j.measurement.2010.08.003
- Hyvarinen , A. and Oja , E. Independent Component Analysis: Algorithms and Applications Neural Networks 13 4-5 411 430 2000 10.1016/s0893-6080(00)00026-5
- Du , X.F. , Li , Z.J. , Bi , F.R. , Zhang , J.H. et al. Source Separation of Diesel Engine Vibration Based on the Empirical Mode Decomposition and Independent Component Analysis Chinese Journal of Mechanical Engineering 25 3 557 563 2012 10.3901/CJME.2012.03.557
- Zarzoso , V. and Comon , P. Robust Independent Component Analysis by Iterative Maximization of the Kurtosis Contrast with Algebraic Optimal Step Size IEEE Transactions on Neural Networks 21 2 248 261 2010 10.1109/TNN.2009.2035920