A Non-Parametric Empirical Method for Nonlinear and Non-Stationary Signal Analysis

Authors

  • Y. Berrouche LIS laboratory, Department of Electronics, Faculty of Technology, Ferhat Abbes Sétif 1 University, Algeria
Volume: 12 | Issue: 1 | Pages: 8058-8062 | February 2022 | https://doi.org/10.48084/etasr.4651

Abstract

A Non-parametric Ensemble Empirical Mode Decomposition (NCEEMD) method is a novel technique for nonlinear and non-stationary signal analysis to detect a gearbox fault. The NCEEMD method was based on the CEEMD, but the Gaussian white noise was replaced by the fractional Gaussian noise. The NCEEMD method does not need to choose the appropriate SNR and the number of ensemble trials before signal processing, which makes it a non-parametric method. This new approach was evaluated using a simulated malfunction signal representing two typical faults in gearbox systems: modulation and rub-impact. Its performance was evaluated in terms of MSE and computation time. A comparative study between the EMD, EEMD, CEEMD, and NCEEMD methods showed that the latter performed better by improving the computation time and accuracy of CEEMD. The proposed method is a non-parametric method that provides a powerful tool in extracting the modulation and the rub-impact features from a vibration signal. The NCEEMD method helps to track down the gearbox faults and resolve this crucial problem in mechanical machines.

Keywords:

complementary ensemble empirical mode decomposition, fractional Gaussian noise, gearbox fault detection, nonlinear signal analysis

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References

S. J. Loutridis, "Damage detection in gear systems using empirical mode decomposition," Engineering Structures, vol. 26, no. 12, pp. 1833–1841, Oct. 2004. DOI: https://doi.org/10.1016/j.engstruct.2004.07.007

X. Wang, V. Makis, and M. Yang, "A wavelet approach to fault diagnosis of a gearbox under varying load conditions," Journal of Sound and Vibration, vol. 329, no. 9, pp. 1570–1585, Apr. 2010. DOI: https://doi.org/10.1016/j.jsv.2009.11.010

J. Rafiee, M. A. Rafiee, and P. W. Tse, "Application of mother wavelet functions for automatic gear and bearing fault diagnosis," Expert Systems with Applications, vol. 37, no. 6, pp. 4568–4579, Jun. 2010. DOI: https://doi.org/10.1016/j.eswa.2009.12.051

Z. K. Peng and F. L. Chu, "Application of the wavelet transform in machine condition monitoring and fault diagnostics: a review with bibliography," Mechanical Systems and Signal Processing, vol. 18, no. 2, pp. 199–221, Mar. 2004. DOI: https://doi.org/10.1016/S0888-3270(03)00075-X

N. E. Huang et al., "The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis," Proceedings of the Royal Society of London. Series A: Mathematical, Physical and Engineering Sciences, vol. 454, no. 1971, pp. 903–995, Mar. 1998. DOI: https://doi.org/10.1098/rspa.1998.0193

G. Gai, "The processing of rotor startup signals based on empirical mode decomposition," Mechanical Systems and Signal Processing, vol. 20, no. 1, pp. 222–235, Jan. 2006. DOI: https://doi.org/10.1016/j.ymssp.2004.07.001

C. Junsheng, Y. Dejie, and Y. Yu, "The application of energy operator demodulation approach based on EMD in machinery fault diagnosis," Mechanical Systems and Signal Processing, vol. 21, no. 2, pp. 668–677, Feb. 2007. DOI: https://doi.org/10.1016/j.ymssp.2005.10.005

J. Cheng, D. Yu, J. Tang, and Y. Yang, "Application of frequency family separation method based upon EMD and local Hilbert energy spectrum method to gear fault diagnosis," Mechanism and Machine Theory, vol. 43, no. 6, pp. 712–723, Jun. 2008. DOI: https://doi.org/10.1016/j.mechmachtheory.2007.05.007

Q. Gao, C. Duan, H. Fan, and Q. Meng, "Rotating machine fault diagnosis using empirical mode decomposition," Mechanical Systems and Signal Processing, vol. 22, no. 5, pp. 1072–1081, Jul. 2008. DOI: https://doi.org/10.1016/j.ymssp.2007.10.003

F. Wu and L. Qu, "Diagnosis of subharmonic faults of large rotating machinery based on EMD," Mechanical Systems and Signal Processing, vol. 23, no. 2, pp. 467–475, Feb. 2009. DOI: https://doi.org/10.1016/j.ymssp.2008.03.007

Z. Wu and N. E. Huang, "Ensemble empirical mode decomposition: a noise-assisted data analysis method," Advances in Adaptive Data Analysis, vol. 1, no. 1, pp. 1–41, Jan. 2009. DOI: https://doi.org/10.1142/S1793536909000047

Y. Lei, Z. He, and Y. Zi, "Application of the EEMD method to rotor fault diagnosis of rotating machinery," Mechanical Systems and Signal Processing, vol. 23, no. 4, pp. 1327–1338, May 2009. DOI: https://doi.org/10.1016/j.ymssp.2008.11.005

Y. Lei, Z. He, and Y. Zi, "EEMD method and WNN for fault diagnosis of locomotive roller bearings," Expert Systems with Applications, vol. 38, no. 6, pp. 7334–7341, Jun. 2011. DOI: https://doi.org/10.1016/j.eswa.2010.12.095

Y. Zhou, T. Tao, X. Mei, G. Jiang, and N. Sun, "Feed-axis gearbox condition monitoring using built-in position sensors and EEMD method," Robotics and Computer-Integrated Manufacturing, vol. 27, no. 4, pp. 785–793, Aug. 2011. DOI: https://doi.org/10.1016/j.rcim.2010.12.001

J. R. Yeh, J. S. Shieh, and N. E. Huang, "Complementary ensemble empirical mode decomposition: a novel noise enhanced data analysis method," Advances in Adaptive Data Analysis, vol. 2, no. 2, pp. 135–156, Apr. 2010. DOI: https://doi.org/10.1142/S1793536910000422

G. Rilling, P. Flandrin, and P. Goncalves, "Empirical mode decomposition, fractional Gaussian noise and Hurst exponent estimation," in Proceedings. (ICASSP ’05). IEEE International Conference on Acoustics, Speech, and Signal Processing, 2005., Mar. 2005, vol. 4, pp. 489-492 Vol. 4.

V. Ingle, S. Kogon, and D. Manolakis, Statisical and Adaptive Signal Processing. Northwood, MA, US: Artech, 2005.

J. Zhang, R. Yan, R. X. Gao, and Z. Feng, "Performance enhancement of ensemble empirical mode decomposition," Mechanical Systems and Signal Processing, vol. 24, no. 7, pp. 2104–2123, Oct. 2010. DOI: https://doi.org/10.1016/j.ymssp.2010.03.003

X. Fan and M. J. Zuo, "Gearbox fault detection using Hilbert and wavelet packet transform," Mechanical Systems and Signal Processing, vol. 20, no. 4, pp. 966–982, May 2006. DOI: https://doi.org/10.1016/j.ymssp.2005.08.032

R. E. Bekka and Y. Berrouche, "Improvement of ensemble empirical mode decomposition by over-sampling," Advances in Adaptive Data Analysis, vol. 5, no. 3, Jul. 2013, Art. no. 1350012. DOI: https://doi.org/10.1142/S179353691350012X

I. Tellala, N. Amardjia, and A. Kesmia, "Α Modified EMD-ACWA Denoising Scheme using a Noise-only Model," Engineering, Technology & Applied Science Research, vol. 10, no. 2, pp. 5470–5476, Apr. 2020. DOI: https://doi.org/10.48084/etasr.3406

W. Mohguen and S. Bouguezel, "Denoising the ECG Signal Using Ensemble Empirical Mode Decomposition," Engineering, Technology & Applied Science Research, vol. 11, no. 5, pp. 7536–7541, Oct. 2021. DOI: https://doi.org/10.48084/etasr.4302

A. Y. Al-Rawashdeh, "Investigation of an Induction Wound Rotor Motor to Work as a Synchronous Generator," Engineering, Technology & Applied Science Research, vol. 9, no. 2, pp. 4071–4074, Apr. 2019. DOI: https://doi.org/10.48084/etasr.2606

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How to Cite

[1]
Y. Berrouche, “A Non-Parametric Empirical Method for Nonlinear and Non-Stationary Signal Analysis”, Eng. Technol. Appl. Sci. Res., vol. 12, no. 1, pp. 8058–8062, Feb. 2022.

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