The Research on the Methodology of Diagnosing the Fault of Bearing in Warships Based on NS-EMD

Article Preview

Abstract:

EMD is now a commonly used nonlinear and instable signal processing method, but it has boundary runaway and modal aliasing. The single disunited IMF cannot well reflect the characteristics of the respective vibration source. Therefore, in order to suppress the boundary runaway that will appear in the process of EMD, the image method is used to extend the length of signal data. To solve the modal aliasing, it is necessary to decompose the extended data by the EMD method, to distinguish the IMF that produces modal aliasing after decomposition, to integrate it according to the integrity of the EMD and then to re-decompose it after adding broadband white noise with the average value of zero. On the basis of that, it is better to improve NS-EMD method and realize the AM-FM demodulation by standardized method. By the spectrum analysis, we extract the fault characteristics of rolling bearings and propose a method to diagnose faults of rolling bearing. The results of analyzing the simulation and the vibration signal of fault rolling bearing shows that the method can effectively extract fault characteristics of rolling bearing.

You might also be interested in these eBooks

Info:

Periodical:

Pages:

352-357

Citation:

Online since:

June 2013

Export:

Price:

Permissions CCC:

Permissions PLS:

Сopyright:

© 2013 Trans Tech Publications Ltd. All Rights Reserved

Share:

Citation:

[1] Norden.E. The empirical mode decomposition and the Hilbert spectrum for nonlinear and non2stationary time series analysis. [J] Proc R SocLond, 1998, 454: 903- 995.

Google Scholar

[2] Yu Dejie, Cheng Junsheng, Yang Yu. Application of the Hilbert-Huang transform method to roller bearing fault diagnosis [J]. China Mechanical Engineering, 2003, 14(24): 2140-2142.

Google Scholar

[3] Yu Dejie, Cheng Junsheng, Yang Yu. A fault diagnosis approach for roller bearings based on EMD method and AR model [J]. 2004, 17(3): 332-335.

DOI: 10.1016/j.ymssp.2004.11.002

Google Scholar

[4] Gao Qiang, Du Xiaoshan, Fan Hong, et al. An empirical mode decomposition based method for rolling bearing fault diagnosis[J]. 2007, 20(1): 15-18.

Google Scholar

[5] Norden.E., Samuel S. The Hilbert-Huang Transform and Its Application [J], Interdisciplinary Mathematical Sciences-Vol.5,2007, 14-29.

Google Scholar

[6] Chen Qiuhui, Norden E., Sherman R. et al. A B-spline approach for empirical mode decompositions [J]. Advances in Computational Mathematics 2006, 24: 171-195.

Google Scholar

[7] Deng Yongjun, Wang Wei, Boundary processing technique in EMD method and Hilbert transform [J]. Chinese Science Bullentin, 2001, 146(11): 257-263.

Google Scholar