DOI QR코드

DOI QR Code

A New Approach for Detection of Gear Defects using a Discrete Wavelet Transform and Fast Empirical Mode Decomposition

  • TAYACHI, Hana (University of Tunis El Manar, National Engineering School of Tunis, Images and Information Technologies Laboratory) ;
  • GABZILI, Hanen (University of Tunis El Manar, National Engineering School of Tunis, Images and Information Technologies Laboratory) ;
  • LACHIRI, Zied (University of Tunis El Manar, National Engineering School of Tunis, Images and Information Technologies Laboratory)
  • 투고 : 2022.02.05
  • 발행 : 2022.02.28

초록

During the past decades, detection of gear defects remains as a major problem, especially when the gears are subject to non-stationary phenomena. The idea of this paper is to mixture a multilevel wavelet transform with a fast EMD decomposition in order to early detect gear defects. The sensitivity of a kurtosis is used as an indicator of gears defect burn. When the gear is damaged, the appearance of a crack on the gear tooth disrupts the signal. This is due to the presence of periodic pulses. Nevertheless, the existence of background noise induced by the random excitation can have an impact on the values of these temporal indicators. The denoising of these signals by multilevel wavelet transform improves the sensitivity of these indicators and increases the reliability of the investigation. Finally, a defect diagnosis result can be obtained after the fast transformation of the EMD. The proposed approach consists in applying a multi-resolution wavelet analysis with variable decomposition levels related to the severity of gear faults, then a fast EMD is used to early detect faults. The proposed mixed methods are evaluated on vibratory signals from the test bench, CETIM. The obtained results have shown the occurrence of a teeth defect on gear on the 5th and 8th day. This result agrees with the report of the appraisal made on this gear system.

키워드

참고문헌

  1. Wei-tao Du, Qiang Zeng1, Yi-min Shao, Li-ming Wang and Xiao-xi Ding, Multi-Scale Demodulation for Fault Diagnosis Based on a Weighted-EMD De-Noising Technique and Time-Frequency Envelope Analysis, Applied Sciences (2020)
  2. Hongguang Li, Yue Hu, Fucai Li, Guang Meng, Succinct and fast empirical mode decomposition, Mech. Syst. Signal Process85 (2017) 879-895 https://doi.org/10.1016/j.ymssp.2016.09.031
  3. S.M.A. Bhuiyan, R.R. Adhami, J.F. Khan, A novel approach of fast and adaptive bidimensional empirical mode decomposition, IEEE Int. Conf. Acoust. Speech Signal Process. 2 (2008) 1313-1316.
  4. Z. Zhang, Y. Zhang, Y. Zhu, A new approach to analysis of surface topography, Precis. Eng. 34 (4) (2010) 807-810. https://doi.org/10.1016/j.precisioneng.2010.05.002
  5. S. Charleston-Villalobos, R. Gonzalez-Camarena, G. Chi-Lem, T. Aljama-Corrales, Crackle sounds analysis by empirical mode decomposition, IEEE Eng. Med. Biol. Mag. 26 (2007) 40-47. https://doi.org/10.1109/MEMB.2007.289120
  6. R. Srinivasan, R. Rengaswamy, R. Miller, A modified empirical mode decomposition (EMD) process for oscillation characterization in control loops, Control Eng. Pract. 15 (9) (2007) 1135-1148. https://doi.org/10.1016/j.conengprac.2007.01.014
  7. L. Luo, Y. Yan, P. Xie, J. Sun, Y. Xu, J. Yuan, Hilbert-Huang transform, Hurst and chaotic analysis based flow regime identification methods for an airlift reactor, Chem. Eng. J. 181-182 (2012) 570-580. https://doi.org/10.1016/j.cej.2011.11.093
  8. G. Xu, W. Tian, L. Qian, EMD- and SVM-based temperature drift modeling and compensation for a dynamically tuned gyroscope (DTG), Mech. Syst. Signal Process. 21 (8) (2007) 3182-3188. https://doi.org/10.1016/j.ymssp.2007.05.006
  9. C. Capdessus, M. Sidahmed, Analysis of the vibrations of a cepstrum gear, correlation, spectrum, signal processing, Vol. 8, No. 5, pp. 365-371, 1992.
  10. J. Tang, L. Zhao, H. Yue, W. Yu, T. Chai, Vibration analysis based on empirical mode decomposition and partial least square, Procedia Eng. 16 (2011) 646-652. https://doi.org/10.1016/j.proeng.2011.08.1136
  11. Z. Zhang, Y. Zhang, Y. Zhu, A new approach to analysis of surface topography, Precis. Eng. 34 (4) (2010) 807-810. https://doi.org/10.1016/j.precisioneng.2010.05.002
  12. S. Charleston-Villalobos, R. Gonzalez-Camarena, G. Chi-Lem, T. Aljama-Corrales, Crackle sounds analysis by empirical mode decomposition, IEEE Eng. Med. Biol. Mag. 26 (2007) 40-47. https://doi.org/10.1109/MEMB.2007.289120
  13. E. Ambikairajah, Emerging features for speaker recognition, 2007 6th Int. Conf. Information, Commun. Signal Process. ICICS. (2007).
  14. Y.B. Yang, K.C. Chang, Extraction of bridge frequencies from the dynamic response of a passing vehicle enhanced by the EMD technique, J. Sound Vib. 322 (4-5) (2009) 718-739. https://doi.org/10.1016/j.jsv.2008.11.028
  15. H. Zhang, X. Qi, X. Sun, S. Fan, Application of Hilbert-Huang transform to extract arrival time of ultrasonic lamb waves, ICALIP 2008 Int. Conf. Audio Lang. Image Process. Proc. (2008) 1-4.
  16. C. Capdessus, M. Sidahmed, Analyse des vibrations d'un engrenage cepstre, correlation, spectre, traitement du signal, Vol. 8, n° 5, pp. 365-371, 1992.
  17. El Badaoui, F. Guillet, J. Daniere, "New applications of the real cepstrum to gear signals, including definition of a robust fault indicator, " Mechanical Systems and Signal Processing 18, 2004, pp.1031-1046. https://doi.org/10.1016/j.ymssp.2004.01.005
  18. Tong Wang, Mingcai Zhang, Qihao Yu, Huyuan Zhang . Comparing the applications of EMD and EEMD on time-frequency analysis of seismic signal, Journal of Applied Geophysics 83 (2012) 29-34 https://doi.org/10.1016/j.jappgeo.2012.05.002
  19. P. Shi, C. Su, D. Han, Fault diagnosis of rotating machinery based on adaptive stochastic resonance and AMD-EEMD, Shock Vib. 2016 (2016) 1-11.
  20. X. Xue, J. Zhou, Y. Xu, W. Zhu, C. Li, An adaptively fast ensemble empirical mode decomposition method and its applications to rolling element bearing fault diagnosis, Mech. Syst. Signal Process. 62-63 (2015) 444-459. https://doi.org/10.1016/j.ymssp.2015.03.002
  21. H. GABZILI1, Z. LACHIRI1, M. BADAOUI, Fault detection in gears by wavelet thresholding analysis, Surveillance 8, international conference, Octobre 2015, Roanne, France.
  22. A. BENZINEB, H. GABZILI, Z. LACHIRI, Multilevel decomposition of the envelope for faults detection in gears, SSS'18, international conference, Mai 2018
  23. Huang, N.E.; Shen, Z.; Long, S.R.; Wu, M.C.; Shih, H. H.; Zheng, Q.; Liu, H.H. The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis//Proceedings of the Royal Society of London A: Mathematical, physical and engineering sciences. R. Soc. 1998, 454, 903-995 https://doi.org/10.1098/rspa.1998.0193
  24. Li, Y.; Xu, M.;Wei, Y.; Huang,W. An improvement emd method based on the optimized rational hermite interpolation approach and its application to gear fault diagnosis. Measuremen 2015, 63, 330-345.
  25. C. Capdessus, M. Sidahmed, Cyclostationary processes application in gear faults early diagnosis, Mech. Syst. Signal Process. 14 (2000) 371-685 https://doi.org/10.1006/mssp.1999.1260
  26. A. Parey, M. El Badaoui, F. Guillet, N. Tandon, Dynamic modeling of spur gear pair and application of empirical mode decomposition-based statistical analysis for early detection of localized tooth defect, J. Sound Vib. 294 (2006) 547-561 https://doi.org/10.1016/j.jsv.2005.11.021
  27. M. El Badaoui, F. Guillet, J. Daniere, New applications of the real cepstrum to gear signals, including definition of a robust fault indicator, Mech. Sys. Signal Process. 18, (2004) 1031-1046 https://doi.org/10.1016/j.ymssp.2004.01.005
  28. L. Bouillaut, Approches cyclostationnaire et non lineaire pour l'analyse vibratoire de machines tournantes: Aspects th'eoriques et applications au diagnostic, These Universite de Technologie de Compiegne, 7 novembre 2000
  29. Ma, H.; Pang, X.; Feng, R.; Song, R.; Wen, B. Fault features analysis of cracked gear considering the effects of the extended tooth contact. Eng. Fail. Anal. 2015, 48, 105-120. https://doi.org/10.1016/j.engfailanal.2014.11.018
  30. Lei, Y.; Kong, D.; Lin, J.; Zuo, M.J. Fault detection of planetary gearboxes using new diagnostic parameters. Meas. Sci. Technol. 2012,
  31. H. Mahgoun, R. E.Bekka, A.Felkaoui, Gearbox fault diagnosis using ensemble empirical mode decomposition (EEMD) and residual signal, Mechanics & Industry, Vol. 13, n° 1, pp. 33-44, 2012. https://doi.org/10.1051/meca/2011150