Empirical Mode Decomposition of Vibration Signal for Detection of Local Disturbances in Planetary Gearbox Used in Heavy Machinery System

Article Preview

Abstract:

In rotating machinery, the detection of local damage is one of the most important issues. This kind of change of technical condition produce local disturbance according to temporal (local) change of stiffness of kinematic pair (tooth-tooth contact, rolling element-outer/inner race etc). In many practical, i.e. industrial cases, vibration signature of such change is weak in sense of produced energy, so consequently, completely masked by other vibration sources in machine. The general concept of signal processing for local damage detection is to use so called signal enhancement, i.e. a kind of tool that may improve signal to noise ratio. One may find many approaches used in the literature. Most of them use signal filtering (classical, adaptive and optimal filters), decomposition (wavelets) or extraction (blind source separation). Empirical Mode Decomposition (EMD) is one of such techniques that can be used with signal decomposition problem. In this paper, EMD will be used for vibration signal decomposition in order to extract information about local perturbation of arm (carrier) in planetary gearbox used in heavy mining machine, i.e. bucket wheel excavator. As a result of application of EMD, one may obtain several time series with different properties of sub-signal. Due to predefined task, namely local disturbance detection, several criteria have been investigated in order to select the most informative empirical mode. First criterion was kurtosis calculated for every mode with very simple decision rule (max kurtosis is the best). It was found that such approach is not optimal due to some random impulses that are not related to damage. To improve results, it is proposed to combine envelope spectrum and kurtosis. If envelope spectrum contains family of components related to arm (carrier) shaft frequency and signal is spiky (kurtosis is high) result of EMD for given mode is optimal in sense of carried information. However, in this approach decision was made based on visual inspection of the envelope spectra of each mode, which is non-effective way. Finally two parameters have been proposed: 1) Pearson correlation coefficient of an empirical mode and the empirically determined local mean of original signal; 2) a relative power of an empirical mode.

You might also be interested in these eBooks

Info:

Periodical:

Pages:

109-116

Citation:

Online since:

October 2013

Export:

Price:

Permissions CCC:

Permissions PLS:

Сopyright:

© 2014 Trans Tech Publications Ltd. All Rights Reserved

Share:

Citation:

[1] P.D. Samuel, D.J. Pines, A review of vibration-based techniques for helicopter transmission diagnostics, Journal of Sound and Vibration, 282/1–2 (2005) 475-508.

DOI: 10.1016/j.jsv.2004.02.058

Google Scholar

[2] F. Combet, L. Gelman, Optimal filtering of gear signals for early damage detection based on the spectral kurtosis, Mechanical Systems and Signal Processing 23/3 (2009) 652-668.

DOI: 10.1016/j.ymssp.2008.08.002

Google Scholar

[3] J. Urbanek, J. Antoni, T. Barszcz, Detection of signal component modulations using modulation intensity distribution, Mechanical Systems and Signal Processing 28 (2012) 399-413.

DOI: 10.1016/j.ymssp.2011.12.018

Google Scholar

[4] T. Barszcz, A. Jablonski, A Novel Method of Optimal Band Selection for Vibration Signal Demodulation, Mechanical Systems and Signal Processing 25/1 (2011) 431-451.

DOI: 10.1016/j.ymssp.2010.05.018

Google Scholar

[5] T. Barszcz, Decomposition Of Vibration Signals Into Deterministic And Nondeterministic Components And Its Capabilities Of Fault Detection And Identification, Int. J. Appl. Math. Comput. Sci., 19/2 (2009) 327-335.

DOI: 10.2478/v10006-009-0028-0

Google Scholar

[6] R. Zimroz, W. Bartelmus, Application of adaptive filtering for weak impulsive signal recovery for bearings local damage detection in complex mining mechanical systems working under condition of varying load, Diffusion and Defect Data Pt.B: Solid State Phenomena 180 (2012) 250-257.

DOI: 10.4028/www.scientific.net/SSP.180.250

Google Scholar

[7] J. Antoni, Cyclostationarity by examples, Mech. Syst. and Signal Proc. 23/4 (2009) 987-1036.

Google Scholar

[8] R. Zimroz, W. Bartelmus, Gearbox condition estimation using cyclo-stationary properties of vibration signal, Key Engineering Materials 413-414 (2009) 471-478.

DOI: 10.4028/www.scientific.net/kem.413-414.471

Google Scholar

[9] J. Lin, M. Zuo, Gearbox fault diagnosis using adaptive wavelet filter, Mechanical Systems and Signal Processing 17/6 (2003) 1259-1269.

DOI: 10.1006/mssp.2002.1507

Google Scholar

[10] P. Flandrin, G. Rilling, P. Gonçalvès, Empirical Mode Decomposition as a Filter Bank, IEEE Signal Processing Letters 11/2 (2004) 112–114.

DOI: 10.1109/lsp.2003.821662

Google Scholar

[11] B. Liu, S. Riemenschneider, Y. Xu, Gearbox fault diagnosis using empirical mode decomposition and Hilbert spectrum, Mech. Systems and Signal Proc. 20/ 3 (2006) 718–734.

DOI: 10.1016/j.ymssp.2005.02.003

Google Scholar

[12] J. Dybała, R. Zimroz, Application of Empirical Mode Decomposition for impulsive signal extraction to detect bearing damage – industrial case study, in: Fakhfakh T. et al . (Eds.) Condition Monitoring of Machinery in Non-Stationary Operations, Part 3, Springer, (2012), p.257–266.

DOI: 10.1007/978-3-642-28768-8_27

Google Scholar

[13] R.A. Makowski, R. Zimroz, Adaptive bearings vibration modeling for diagnosis, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) 6943 LNAI (2011) 248-259.

DOI: 10.1007/978-3-642-23857-4_26

Google Scholar

[14] R. Makowski, R. Zimroz, A procedure for weighted summation of the derivatives of reflection coefficients in adaptive Schur filter with application to fault detection in rolling element bearings Mechanical Systems and Signal Processing (2012).

DOI: 10.1016/j.ymssp.2012.05.005

Google Scholar

[15] R. Makowski, R. Zimroz, Application of Schur Filtering for Local Damage Detection in Gearboxes, in: T. Fakhfakh, et al . (Eds.) Condition Monitoring of Machinery in Non-Stationary Operations Part 3, Springer, pp.301-308.

DOI: 10.1007/978-3-642-28768-8_32

Google Scholar

[16] J. Urbanek, T. Barszcz, R. Zimroz, J. Antoni, Application of averaged instantaneous power spectrum for diagnostics of machinery operating under non-stationary operational conditions Measurement: Journal of the Internat. Measurement Confederation 45(7) (2012) 1782-1791.

DOI: 10.1016/j.measurement.2012.04.006

Google Scholar

[17] M.T. Khabou, N. Bouchaala, F. Chaari, T. Fakhfakh, M. Haddar, Study of a spur gear dynamic behavior in transient regime, Mech. Systems and Signal Proc. 25/8 (2011) 3089-3101

DOI: 10.1016/j.ymssp.2011.04.018

Google Scholar

[18] L. Gelman et al, Adaptive vibration condition monitoring technology for local tooth damage in gearboxes, Insight: Non-Destructive Testing and Condition Monitoring 47(8) (2005) 461-464.

DOI: 10.1784/insi.2005.47.8.461

Google Scholar

[19] A.O. Boudraa, J.C. Cexus, EMD-based signal filtering. IEEE Transactions on Instrumentation and Measurement, 56/6 (2007) 2196–2202.

DOI: 10.1109/tim.2007.907967

Google Scholar

[20] D. Yu, J. Cheng, Y. Yang, Application of EMD method and Hilbert spectrum to the fault diagnosis of roller bearings, Mechanical Systems and Signal Processing 19/2 (2005) 259–270.

DOI: 10.1016/s0888-3270(03)00099-2

Google Scholar

[21] N.E. Huang, Z. Shen, S.R. Long, M.L.C. Wu, H.H. Shih, Q.N. Zheng, N.C. Yen, C.C. Tung, H.H. Liu, The empirical mode decomposition and the Hilbert spectrum for nonlinear and non–stationary time series analysis, Proc. of the Royal Society of London, Series A – Mathematical Physical and Engineering Sciences, vol. 454, (1998), p.903–995.

DOI: 10.1098/rspa.1998.0193

Google Scholar

[22] G. Rilling, P. Flandrin, P. Gonçalvès, On Empirical Mode Decomposition and its algorithms, Proceedings of the 6th IEEE-EURASIP Workshop on Nonlinear Signal and Image Processing (NSIP-03), Grado, Italy, June 8-11, (2003), áAvailable at: http://perso.ens-lyon.fr/patrick.flandrin/NSIP03.pdfñ, (July 2012).

DOI: 10.1109/icassp.2005.1416052

Google Scholar