Feasibility of Using Wavelet Analysis and Machine Learning Method in Technical Diagnosis of Car Seats
Abstract
This paper presents the results of preliminary research aimed at developing a method for rapid, noncontact diagnostics of the electric drive of car seats. The method is based on the analysis of acoustic signals produced during the operation of the drive. Pattern recognition and machine learning processes were used in the diagnosis. A method of feature extraction (diagnostic symptoms) using wavelet decomposition of acoustic signals was developed. The discriminative properties of a set of diagnostic symptoms were tested sing the “Classification Learner” application available in MATLAB. The obtained results confirmed the usefulness of the developed method for the technical diagnostics of car seats.Keywords:
acoustic diagnostics, wavelet decomposition, machine learningReferences
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2. Batko W., Dabrowski Z., Engel Z., Kicinski J., Weyna S. (2005), Modern Methods of Vibroacoustics Processes Research [in Polish: Nowoczesne Metody Badania Procesów Wibroakustycznych], Wydawnictwo Instytutu Technologii Eksploatacji – PIB, Radom.
3. Białasiewicz J. (2004), Wavelets and Approximations [in Polish: Falki i Aproksymacje], WNT, Warszawa.
4. Ding L., Peng J., Zhang X., Song L. (2023), Sleep snoring sound recognition based on wavelet packet transform, Archives of Acoustics, 48(1): 3–12, https://doi.org/10.24425/aoa.2022.142906
5. Duda R., Hart P. (1973), Pattern Classification and Scene Analysis, John Wiley & Sons, Inc.
6. Głowacz A. (2014), Diagnostics of synchronous motor based on analysis of acoustic signals with the use of line spectral frequencies and K-nearest neighbor classifier, Archives of Acoustics, 39(2): 189–194, https://doi.org/10.2478/aoa-2014-0022
7. Han X., Xu J., Song S., Zhou J. (2022), Crack fault diagnosis of vibration exciter rolling bearing based on genetic algorithm-optimized Morlet wavelet filter and empirical mode decomposition, International Journal of Distributed Sensor Networks, 18(8), https://doi.org/10.1177/15501329221114566
8. Huang W.Y., Solorzano M.R. (1971), Wavelet Preprocessing of Acoustic Signals, Naval Ocean System Center.
9. International Organization for Standardization (2010), Acoustics – Determination of sound power levels and sound energy levels of noise sources using sound pressure – Engineering methods for an essentially free field over a reflecting plane (ISO Standard No. 3744:2010), https://www.iso.org/standard/52055.html
10. Lin J. (2001), Feature extraction of machine sound using wavelet and its application in fault diagnosis, NDT & E International, 34(1): 25–30, https://doi.org/10.1016/S0963-8695%2800%2900025-6
11. Lin J., Qu L. (2000), Feature extraction based on Morlet wavelet and its application for mechanical fault diagnosis, Journal of Sound and Vibration, 234(1): 135–148, https://doi.org/10.1006/jsvi.2000.2864
12. Mallat S.G. (1989), A theory for multiresolution signal decomposition: the wavelet representation, IEEE Transactions on Pattern Analysis and Machine Intelligence, 11(7): 674–693, https://doi.org/10.1109/34.192463
13. Pawlik P. (2019), The use of the acoustic signal to diagnose machines operated under variable load, Archives of Acoustics, 45(2): 263–270, https://doi.org/10.24425/aoa.2020.133147
14. Peng Z.K., Chu F.L. (2004), Application of the wavelet transform in machine condition monitoring and fault diagnostics: A review with bibliography, Mechanical Systems and Signal Processing, 18(2): 199–221, https://doi.org/10.1016/S0888-3270%2803%2900075-X
15. Piersol J.S. (1989), Random Data, John Wiley & Sons.
16. Qiu H., Lee J., Lin J., Yu G. (2006), Wavelet filter-based weak signature detection method and its application on rolling element bearing prognostics, Journal of Sound and Vibration, 289(4–5): 1066–1090, https://doi.org/10.1016/j.jsv.2005.03.007
17. Sobczak W., Malina W. (1985), Information Selection and Reduction Methods [in Polish: Metody Selekcji i Redukcji Informacji], WNT, Warszawa.
18. Staszewski W.J. (1998), Wavelet-based compression and feature selection for vibration analysis, Journal of Sound and Vibration, 211(5): 735–760, https://doi.org/10.1006/jsvi.1997.1380
19. Sun Q., Chen P., Zhang D., Xi F. (2004), Pattern recognition for automatic machinery fault diagnosis, Journal of Vibration and Acoustics, 126(2): 307–316, https://doi.org/10.1115/1.1687391
20. Syed S.H., Muralidharan V. (2022), Feature extraction using discrete wavelet transform for fault classification of planetary gearbox – A comparative study, Applied Acoustics, 188: 108572, https://doi.org/10.1016/j.apacoust.2021.108572
21. Szabatin J. (2000), Basics of Signal Theory [in Polish: Podstawy Teorii Sygnałów], WKŁ, Warszawa.
22. Tang B., Liu W., Song T. (2010), Wind turbine fault diagnosis based on Morlet wavelet transformation and Wigner-Ville distribution, Renewable Energy, 35(12): 2862–2866, https://doi.org/10.1016/j.renene.2010.05.012
23. Xi F., Sun Q., Krishnappa G. (1997), Bearing diagnostics based on pattern recognition of statistical parameters, [in:] 5th International Congress on Sound and Vibration, Adelaide, South Australia.
24. Yang J., Zhou C., Li X. (2022), Research on fault feature extraction method based on parameter optimized variational mode decomposition and robust independent component analysis, Coatings, 12(3): 419, https://doi.org/10.3390/coatings12030419
