A New Purification Method for Rotor Center’s Orbit by Using Ensemble Empirical Mode Decomposition

Article Preview

Abstract:

. Aiming at the purification of rotor center’s orbit, a new approach was presented by using ensemble empirical mode decomposition (EEMD). Ensemble empirical mode decomposition decomposed a complicated signal into a series of intrinsic mode functions (IMFs). Then according to prior knowledge of rotating machinery, chose some interested IMFs and reconstructed the needed signal. By doing this the noises would be eliminated successfully. At last the purification of rotor center’s orbit was obtained by extracting the useful signal component. Simulation and practical results show the advantage of EEMD in noise de-noising and purification of rotor center’s orbit. This method also has simple algorithm and high calculating speed; it provides a new way for purification of rotor center’s orbit of rotating machinery.

You might also be interested in these eBooks

Info:

Periodical:

Advanced Materials Research (Volumes 986-987)

Pages:

801-804

Citation:

Online since:

July 2014

Export:

Price:

Permissions CCC:

Permissions PLS:

Сopyright:

© 2014 Trans Tech Publications Ltd. All Rights Reserved

Share:

Citation:

* - Corresponding Author

[1] S. T. Wang and H. M. Li, A new method for automatically identifying the axis trace moving direction of turbine-generator unit, Proceeding of the CSEE, 2003, Vol. 23, pp.146-149.

Google Scholar

[2] N. E Huang, Z. Shen, S. R. Long, M. C. Wu, H. H. Shih, Q. N. Zheng, N. C. Yen, C. C. Tung and H. H. Liu, The empirical mode decomposition and the hilbert spectrum for nonlinear and non-stationary time series analysis, Proc. R. Soc. Lond A, Vol. 454, pp.903-995.

DOI: 10.1098/rspa.1998.0193

Google Scholar

[3] J. C. Echeverria, J. A. Crowe, M. S. Woolfon, B. R. Hayes-Gill, Application of empirical mode decomposition to heart rate variability analysis, Medical and Biological Engineering and Computing, Vol. 39, No. 4, pp.471-479.

DOI: 10.1007/bf02345370

Google Scholar

[4] H. L. Liang, S. L. Bressler, R. Desimone and P. Fries, Empirical mode decomposition: a method for analyzing neural data, Neurocomputing, Vol. 65, pp.801-807, 2005.

DOI: 10.1016/j.neucom.2004.10.077

Google Scholar

[5] K. Y. Qi, Z. J. He and Y. Y. Zi, Cosing window-based boundary processing method for EMD and its application in rubbing fault diagnosis, Mechanical Systems and Signal Processing, Vol. 21, No. 7, pp.2750-2760.

DOI: 10.1016/j.ymssp.2007.04.007

Google Scholar

[6] Z. H. 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, 2009, doi: 10. 1142/S1793536909000047.

DOI: 10.1142/s1793536909000047

Google Scholar

[7] J. S. Lin and Q. Chen, Application of the EEMD method to multiple faults diagnosis of gearbox, 2010 2nd International Conference on CD-ROM Purchase at Partner, Mar. 2010, pp.87-90, doi: 10. 1109/ICACC. 2010. 5487143.

DOI: 10.1109/icacc.2010.5486649

Google Scholar