A Rattle Signal Denoising and Enhancing Method Based on Wavelet Packet Decomposition and Mathematical Morphology Filter for Vehicle

• Autorzy: Liang Linyuan , Chen Shuming , Li Peiran

Identyfikatory

DOI

10.24425/aoa.2022.140731

• Języki publikacji: EN

Abstrakty

EN

Buzz, squeak and rattle (BSR) noise has become apparent in vehicles due to the significant reductions in engine noise and road noise. The BSR often occurs in driving condition with many interference signals. Thus, the automatic BSR detection remains a challenge for vehicle engineers. In this paper, a rattle signal denoising and enhancing method is proposed to extract the rattle components from in-vehicle background noise. The proposed method combines the advantages of wavelet packet decomposition and mathematical morphology filter. The critical frequency band and the information entropy are introduced to improve the wavelet packet threshold denoising method. A rattle component enhancing method based on multi-scale compound morphological filter is proposed, and the kurtosis values are introduced to determine the best parameters of the filter. To examine the feasibility of the proposed algorithm, synthetic brake caliper rattle signals with various SNR ratios are prepared to verify the algorithm. In the validation analysis, the proposed method can well remove the disturbance background noise in the signal and extract the rattle components with well SNR ratios. It is believed that the algorithm discussed in this paper can be further applied to facilitate the detection of the vehicle rattle noise in industry.

Słowa kluczowe

EN

rattle signals; wavelet packet decomposition; mathematical morphology filter; critical frequency band; information entropy

• Wydawca: Instytut Podstawowych Problemów Techniki PAN ; Komitet Akustyki PAN ; Polskie Towarzystwo Akustyczne
• Czasopismo: Archives of Acoustics
• Rocznik: 2022
• Tom: Vol. 47, No. 1
• Strony: 43--55
• Opis fizyczny: Biblioogr. 34 poz., fot., rys., tab., wykr.

Twórcy

autor

Liang Linyuan

• State Key Laboratory of Vehicle NVH and Safety Technology Chongqing 401122, China
• State Key Laboratory of Automotive Simulation and Control, Jilin University Changchun 130022, China

autor

Chen Shuming

• State Key Laboratory of Vehicle NVH and Safety Technology Chongqing 401122, China
• State Key Laboratory of Automotive Simulation and Control, Jilin University Changchun 130022, China

autor

Li Peiran

• State Key Laboratory of Vehicle NVH and Safety Technology Chongqing 401122, China

Bibliografia

• 1. Addison P.S. (2002), The Illustrated Wavelet Transform Handbook, Boca Raton: CRC Press, doi: 10.1201/ 9781003040408.
• 2. Bi F., Ma T., Wang X. (2019), Development of a novel knock characteristic detection method for gasoline engines based on wavelet-denoising and EMD decomposition, Mechanical Systems and Signal Processing, 117: 517-536, doi: 10.1016/j.ymssp.2018.08.008.
• 3. Chaudhari N., Mohammed R., Raghavendran P. (2018), A disciplined approach to minimize rattle issues in automotive glove box assembly, SAE Technical Paper, 2018-01-1481, doi: 10.4271/2018-01-1481.
• 4. Chen W., Song H. (2018), Automatic noise attenuation based on clustering and empirical wavelet transform, Journal of Applied Geophysics, 159: 649-665, doi: 10.1016/j.jappgeo.2018.09.025.
• 5. Choi J.M., Lyu S.J., Seol Y.S., Jun I.K., Yi C. (2013), A BSR analytical evaluation method considering the sound quality perception, SAE Technical Paper, 2013-01-1913, doi: 10.4271/2013-01-1913.
• 6. Davies E.R. (2012), Mathematical morphology, [in:] Computer and Machine Vision, 4th ed., Boston: Academic Press.
• 7. Donoho D.L. (1995), De-noising by soft-thresholding, IEEE Transactions on Information Theory, 41(3): 613-627, doi: 10.1109/18.382009.
• 8. Duan T., Li P., Chen S. (2021), Study of rattle noise in vehicle seat system under different excitation signals and loading conditions, SAE Technical Paper, 2020-015230, doi: 10.4271/2020-01-5230.
• 9. Gao R.X., Yan R. (2011a), Continuous wavelet transform, [in:] Wavelets: Theory and Applications for Manufacturing, Gao R.X., Yan R. [Eds], Boston, Ma: Springer US, pp. 33-48.
• 10. Gao R.X., Yan R. (2011b), Discrete wavelet transform, [in:] Wavelets: Theory and Applications for Manufacturing, Gao R.X., Yan R. [Eds], Boston, Ma: Springer US, pp. 49-68.
• 11. General Administration of Quality Supervision (2002), Gb/T 18697-2002: Acoustics - Method for measuring vehicle interior noise, Standards Press of China Beijing, I.a.Q.o.t.P.s.R.o.C., Standardization Administration of The People’s Republic of China.
• 12. Hashim M.A., Nasef M.H., Kabeel A.E., Ghazaly N.M. (2020), Combustion fault detection technique of spark ignition engine based on wavelet packet transform and artificial neural network, Alexandria Engineering Journal, 59(5): 3687-3697, doi: 10.1016/j.aej.2020.06.023.
• 13. Huang H.B., Li R.X., Huang X.R., Lim T.C., Ding W.P. (2016), Identification of vehicle suspension shock absorber squeak and rattle noise based on wavelet packet transforms and a genetic algorithmsupport vector machine, Applied Acoustics, 113: 137- 148, doi: 10.1016/j.apacoust.2016.06.016.
• 14. Li G., Liu X., Tang J., Li J., Ren Z., Chen C. (2020), De-noising low-frequency magnetotelluric data using mathematical morphology filtering and sparse representation, Journal of Applied Geophysics, 172: 103919, doi: 10.1016/j.jappgeo.2019.103919.
• 15. Li H., Wang R., Cao S., Chen Y., Huang W. (2016),A method for low-frequency noise suppression based on mathematical morphology, Geophysics, 81(3): V159-V167, doi: 10.1190/geo2015-0222.1.
• 16. Li H., Wang Y., Wang B., Sun J., Li Y. (2017), The application of a general mathematical morphological particle as a novel indicator for the performance degradation assessment of a bearing, Mechanical Systems and Signal Processing, 82: 490-502, doi: 10.1016/ j.ymssp.2016.05.038.
• 17. Liang L., Chen S., Li P. (2020a), The evaluation of vehicle interior impact noise inducing by speed bumps based on multi-features combination and support vector machine, Applied Acoustics, 163: 107212, doi: 10.1016/j.apacoust.2020.107212.
• 18. Liang L., Chen S., Li P. (2021a), Experiment and evaluation study on rattle noise in automotive seat system, International Journal of Automotive Technology, 22(2): 391-402, doi: 10.1007/s12239-021-0037-z.
• 19. Liang L., Li P., Chen S. (2020b), Further study of the vehicle rattle noise with consideration of the impact rates and loudness, SAE International Journal of Advances and Current Practices in Mobility-V129-99EJ, 2(4): 2285-2296, doi: 10.4271/2020-01-1261.
• 20. Liang L., Li P., Chen S. (2021b), Novel method for identifying and assessing rattle noise on vehicle seatbelt retractors based on time-frequency analysis, SAE Technical Paper, 2021-01-5015, doi: 10.4271/2021-015015.
• 21. Pan J., Chen J., Zi Y., Yuan J., Chen B., He Z. (2016), Data-driven mono-component feature identification via modified nonlocal means and MEWT for mechanical drivetrain fault diagnosis, Mechanical Systems and Signal Processing, 80: 533-552, doi: 10.1016/ j.ymssp.2016.05.013.
• 22. Serra J. (2008), Advances in mathematical morphology: segmentation, [in:] Advances in Imaging and Electron Physics, Hawkes P. [Ed.], Elsevier, pp. 185-219, doi: 10.1016/S1076-5670(07)00004-3.
• 23. Shannon C.E. (1948), A mathematical theory of communication, The Bell System Technical Journal, 27: 379-423.
• 24. Shin S.-H., Cheong C. (2010), Experimental characterization of instrument panel buzz, squeak, and rattle (BSR) in a vehicle, Applied Acoustics, 71(12): 1162-1168, doi: 10.1016/j.apacoust.2010.07.006.
• 25. Tang G., Yan X., Wang X. (2020), Chaotic signal denoising based on adaptive smoothing multiscale morphological filtering, Complexity, 2020: 1-14, doi: 10.1155/2020/7242943.
• 26. Trapp M., Fang C. (2012), Automotive Buzz, Squeak and Rattle, Butterworth-Heinemann: Oxford, doi: 10.1016/B978-0-7506-8496-5.01001-0.
• 27. Wang Y.S., Lee C.M., Kim D.G., Xu Y. (2007), Sound-quality prediction for nonstationary vehicle interior noise based on wavelet pre-processing neural network model, Journal of Sound and Vibration, 299: 933-947, doi: 10.1016/j.jsv.2006.07.034.
• 28. Xing Y.F., Wang Y.S., Shi L., Guo H., Chen H. (2016), Sound quality recognition using optimal wavelet-packet transform and artificial neural network methods, Mechanical Systems and Signal Processing, 66-67: 875-892, doi: 10.1016/j.ymssp.2015.05.003.
• 29. Xu G., Wang J., Zhang Q., Zhang S., Zhu J. (2007), A spike detection method in EEG based on improved morphological filter, Computers in Biology and Medicine, 37(11): 1647-1652, doi: 10.1016/j.comp biomed.2007.03.005.
• 30. Yang G., Liu Y., Wang Y., Zhu Z. (2015), EMD interval thresholding denoising based on similarity measure to select relevant modes, Signal Processing, 109: 95-109, doi: 10.1016/j.sigpro.2014.10.038.
• 31. Yaslan Y., Bican B. (2017), Empirical mode decomposition based denoising method with support vector regression for time series prediction: a case study for electricity load forecasting, Measurement, 103: 52-61, doi: 10.1016/j.measurement.2017.02.007.
• 32. Ying L., Jin-Yan L. (2007), Experimental research on the rule of frequency-band derangement in wavelet packet transform, 2007 IEEE International Conference on Control and Automation, 30 May-1 June 2007, pp. 3099-3102, doi: 10.1109/ICCA.2007.4376931.
• 33. Yue G.-D., Cui X.-S., Zou Y.-Y., Bai X.-T., Wu Y.-H., Shi H.-T. (2019), A Bayesian wavelet packet denoising criterion for mechanical signal with non-Gaussian characteristic, Measurement, 138: 702-712, doi: 10.1016/j.measurement.2019.02.066.
• 34. Zhang X., Wan S., He Y., Wang X., Dou L. (2021), Teager energy spectral kurtosis of wavelet packet transform and its application in locating the sound source of fault bearing of belt conveyor, Measurement, 173: 108367, doi: 10.1016/j.measurement.2020.108367.