EMD Based on Time-Sequence and Window Function and its Application in Diagnosis of Machinery Faults

Abstract:

To solve the end effect occurring in empirical mode decomposition adopted in the course of decomposition, we propose an improved method on the basis of time-sequence analysis and cosine window function. First, the ARMA (Autoregressive Moving Average) of time-varying parameter is adopted to extend signals, and thus the extended data can be smoothly connected with the original signal at the end. Second, the extended signals are processed with cosine window, so that the extended errors will exert no impact on the existing data. Finally, the signals processed as above mentioned will be decomposed with EMD to confine the end effect to the ends of the signal. The simulation and fault signal analysis prove that the proposed method can effectively reduce the impact of the end effect and be applied in rotating machinery fault diagnosis.

• Periodical: Applied Mechanics and Materials (Volume 470)
• Pages: 353-356
• DOI: https://doi.org/10.4028/www.scientific.net/AMM.470.353
• Online since: December 2013
• Authors: Ren Ze Luo*, Peng Cao, Yuan Hua Fu, Ya Bin Huang, Qing Yang
• Keywords: Cosine Window, End Effect, Fault Diagnosis, Time-Varying Parameter

References

[1] Huang N E, The empirical mode decomposition and the hilbert spectrum for nonlinear and non-stationary time series analysis, Proc. R. Soc. Lond. A. 454(1998) 903-995.

DOI: 10.1098/rspa.1998.0193

[2] Cheng J S, Yu D J, Tang J S, Local rub-impact fault diagnosis of the rotor systems based on EMD, Mechanism and Machine Theory. 44(2009) 784-791.

DOI: 10.1016/j.mechmachtheory.2008.04.006

[3] Yaguo Lei, Zhengjia He, Yanyang Zi, Application of the EEMD method to rotor fault diagnosis of rotating machinery, Mechanical System and Signal Processing. 23(2009) 1327-1338.

DOI: 10.1016/j.ymssp.2008.11.005

[4] Junsheng Cheng, Dejie Yu, Jiashi Tang, Application of frequency family separation method based upon EMD and local Hilbert energy spectrum method to gear fault diagnosis, Mechanism Machine Theory. 43(2008) 712-723.

DOI: 10.1016/j.mechmachtheory.2007.05.007

[5] Zhipeng Feng, Ming Liang, Fulei Chu, Recent advances in time–frequency analysis methods for machinery fault diagnosis: A review with application examples, Mechanical Systems and Signal Processing. 38(2013) 165-205.

DOI: 10.1016/j.ymssp.2013.01.017

[6] Shu ZH P, Yang ZH CH, A better method for effectively suppressing end effect of empirical mode decomposition, Journal of Northwestern Polytechnical University. 24(2006) 639-643.

[7] Tong Xu, Jian Wu, Zhen-Sen Wu etal, Long-Term Sunspot Number Prediction based on EMD Analysis and AR Model, Chinese Journal of Astronomy and Astrophysics. 8(2008) 337-342.

DOI: 10.1088/1009-9271/8/3/10

[8] Liu H T, Zhang M, Cheng J X, Dealing with the end issue of EMD based on orthogonal polynomial fitting algorithm, Computer engineering and application. 40(2004) 84-86.

[9] G. F. Bin, J.J. Gao, X.J. Li, etal, Early fault diagnosis of rotating machinery based on wavelet packets—Empirical mode decomposition feature extraction and neural network, Mechanical System and Signal Processing. 27(2012) 696-711.

DOI: 10.1016/j.ymssp.2011.08.002

[10] Cheng J S, Yu D J, Yang Y, Application of support vector regression machines to the recessing of end effects of Hilbert-Huang transform, Mechanical Systems and Signal Processing. 21(2007) 2750-2760.

DOI: 10.1016/j.ymssp.2005.09.005

[11] Shi P M, Ding X J, Li G, etal, An improved method of EMD and its application in rotating machinery fault diagnosis, Journal of Vibration and Shock. 32(2013) 185-190.