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Gearbox fault diagnosis using ensemble empirical modedecomposition (EEMD) and residual signal

Published online by Cambridge University Press:  23 April 2012

Hafida Mahgoun ,
Rais Elhadi Bekka  and
Ahmed Felkaoui
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Abstract

This paper presents the application of new time frequency method, ensemble empirical modedecomposition (EEMD), in purpose to detect localized faults of damage at an early stage.EEMD is a self adaptive analysis method for non-linear and non-stationary signals and itwas recently proposed by Huang and Wu to overcome the drawbacks of the traditionalempirical mode decomposition (EMD). The vibration signal is usually noisy. To detect thefault at an early stage of its development, generally the residual signal is used. Thereexist different methods in literature to calculate the residual signal, in this paper wemention some of them and we propose a new method which is based on EEMD. The results givenby the different methods are compared by using simulated and experimental signals.

Keywords

Ensemble empirical mode decomposition (EEMD)residual signalgearbox fault diagnosisfault detectionrotating machines
Type
Research Article
Information
Mechanics & Industry , Volume 13 , Issue 1 , 2012 , pp. 33 - 44
Copyright
© AFM, EDP Sciences 2012

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References

B.D. Forrester, Use of Wigner Ville distribution in helicopter transmission fault detection in iProc of the Australian, symposium on signal Processing and Applications ASSPA89, Adelaide, Australia, 1989, pp. 77–82
Boulahbal, D., Golnaraghi, M.F., Ismail, F., Amplitude and phase wavelet maps for the detection of cracks in geared systems, Mech. Syst. Signal Process. 13 (1999) 423–436 CrossRefGoogle Scholar
Capdessus, C., Sidahmed, M., Cyclostationary processes application in gear faults early diagnosis, Mech. Syst. Signal Process. 14 (2000) 371–685 CrossRefGoogle Scholar
Bonnardot, F., El Badaoui, M., Randall, R.B., Danière, J., Guillet, F., Use of the acceleration signal of a gearbox in order to perform angular resampling (with limited speed fluctuation), Mech. Syst. Signal Process. 19 (2005) 766–785 CrossRefGoogle Scholar
Gao, Q., Duan, C., Fan, H., Meng, Q., Rotating machine fault diagnosis using empirical mode decomposition, Mech. Syst. Signal Process. 22 (2008) 1072–1081 CrossRefGoogle Scholar
Combet, F., Gelman, L., Optimal filtering of gear signals for early damage detection based on the spectral Kurtosis, Mech. Syst. Signal Process. 23 (2009) 652–668 CrossRefGoogle Scholar
Huang, N.E., Shen, Z., Long, S.R., The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis, Proc. R. Soc. Lond. Ser. 454 (1998) 903–995 CrossRefGoogle Scholar
Huang, N.E., Shen, Z., Long, S.R., A new view of nonlinear water waves : the Hilbert spectrum, Annu. Rev. Fluid Mech. 31 (1999) 417–457 CrossRefGoogle Scholar
Andrade, O., Nasuto, S., Kyberd, P., Sweeney Reed, C.M., Kanijn, F.R.V., EMG signal filtering based on Empirical Mode Decomposition, Biomed. Signal Processing and Control 1 (2006) 44–55 CrossRefGoogle Scholar
Guanlei, Xu, Xiaotong, Wang, Xiaogang, Xu, Improved bi-dimensional EMD and Hilbert spectrum for the analysis of textures, Pattern Recogn. 42 (2009) 718–734 CrossRefGoogle Scholar
Jiang, Rong, Yan, Hong, Studies of spectral properties of short genes using the wavelet subspace Hilbert-Huang transform (WSHHT), Physica A 387 (2008) 4223–4247 CrossRefGoogle Scholar
Li, Xiaoli, Li, Duan, Liang, Zhenhu, Voss, Logan J., Sleigh, Jamie W., Analysis of depth of anesthesia with Hilbert-Huang spectral entropy, Clin. Neurophysiol. 119 (2008) 2465–2475 CrossRefGoogle ScholarPubMed
Yeh, Jia-Rong, Fan, Shou-Zen, Shieh, Jiann-Shing, Human heart beat analysis using a modified algorithm of detrended fluctuation analysis based on empirical mode decomposition, Med. Eng. Phys. 31 (2009) 92–100 CrossRefGoogle ScholarPubMed
Loutridis, S.J., Damage detection in gear systems using empirical mode decomposition, Eng. Struct. 26 (2004) 1833–1841 CrossRefGoogle Scholar
Liu, B., Riemenschneider, S., Xub, Y., Gearbox fault diagnosis using empirical mode decomposition and Hilbert spectrum, Mech. Syst. Signal Process. 17 (2005) 1–17 Google Scholar
Peng, Z.K., Tse, P.W., Chu, F.L., A comparison study of improved Hilbert-Huang transform and wavelet transform : application to fault diagnosis for rolling bearing, Mech. Syst. Signal Process. 19 (2005) 974–988 CrossRefGoogle Scholar
Parey, A., El Badaoui, M., Guillet, F., Tandon, N., 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 CrossRefGoogle Scholar
Li, H., Deng, X., Dai, H., Structural damage detection using the combination method of EMD and wavelet analysis, Mech. Syst. Signal Process. 21 (2007) 298–306 CrossRefGoogle Scholar
Yimin Shao, Fengshou Gu, Fazenda. B. Ball A, Luyang Guan, Gearbox fault diagnosis under different operating conditions based on time synchronous average and ensemble empirical mode decomposition, ICCAS-SICE, 2009, pp. 383–388
Shufeng Ai, Hui Li, Gear Fault Detection Based on Ensemble Empirical Mode Decomposition and Hilbert-Huang Transform Fuzzy Systems and Knowledge Discovery, FSKD ’08, 2008, Vol. 3, pp. 173–177
Flandrin, P., Rilling, G., Goncalve, P., Empirical Mode Decomposition as a Filter Bank, IEEE Signal Process. Lett. 11 (2004) 112–114 CrossRefGoogle Scholar
Huang, N.E., Wu, M.L., Long, S.R., A confidence limit for the empirical mode decomposition and Hilbert spectral analysis, Proc. R. Soc. Lond. 459 (2003) 2317–2345 CrossRefGoogle Scholar
Rato, R.T., Ortigueira, M.D., Batista, A.G., On the HHT, its problems and some solutions, Mech. Syst. Signal Process. 22 (2008) 1374–1394 CrossRefGoogle Scholar
Yanli Yang, Jiahao Deng, Caipeng Wu, Analysis of mode mixing phenomenon in the empirical mode decomposition method, Second Int. Symp. Inf. Sci. Eng. IEEE (2009) 553–556
Zhaohua Wu, N.E. Huang, Ensemble empirical mode decomposition : a noise-assisted data analysis method, advances in adaptive data analysis 1, 2009, 1–41 c world scientific publishing company
R.M. Stewart, Some useful data analysis techniques for gearbox diagnosis. Applications of time series analysis, Ph.D. Thesis, ISVR, University of Southampton, 1977
Wu, Siyan, Zuo, Ming J., Anand Parey, Simulation of spur gear dynamics and estimation of fault growth, J. Sound Vib. 317 (2008) 608–624 CrossRefGoogle Scholar
Mcfadden, P.D., Detecting fatigue cracks in gear by amplitude and phase demodulation of the meshing vibration, ASME Journal of vibration, Acoustics, Stress, and Reliability in Design 108 (1986) 165–170 CrossRefGoogle Scholar
Halim, Enayet B., Shoukat Choudhury, M.A.A., Shah, Sirish L., Zuo, Ming J., Time domain averaging across all scales : A novel method for detection of gearbox faults, Mech. Sys. Signal Process. 22 (2008) 261–278 CrossRefGoogle Scholar
El Badaoui, M., Guillet, F., Daniere, J., New applications of the real cepstrum to gear signals, including definition of a robust fault indicator, Mech. Sys. Signal Process. 18, (2004) 1031–1046 CrossRefGoogle Scholar
L. Bouillaut, Approches cyclostationnaire et non linéaire pour l’analyse vibratoire de machines tournantes : Aspects théoriques et applications au diagnostic, Thèse Université de Technologie de Compiègne, 7 novembre 2000