The Rolling Bearing Fault Diagnosis Research Based on Improved Hilbert-Huang Transformation

Abstract:

For empirical mode decomposition (EMD) of Hilbert-Huang transform (HHT) exists the problem of mode mixing. An analysis method based on ensemble empirical mode decomposition (EEMD) is proposed to apply to fault diagnosis of rolling bearing. This paper puts forward, after signal pretreatment, applying EEMD method to acquire the intrinsic mode function (IMF) of fault signal. Then according to correlation coefficient for IMFs and the signal before decomposing by EEMD method, some redundant low frequency IMFs produced in the process of decomposition can be eliminated, then the effective IMF components are selected to perform a local Hilbert marginal spectrum analysis, then fault characteristics are extracted. Through the vibration analysis of inner-race fault bearing it shows that this method can be effectively applied to extract fault characteristics of rolling bearing.

• Periodical: Applied Mechanics and Materials (Volumes 300-301)
• Pages: 344-350
• DOI: https://doi.org/10.4028/www.scientific.net/AMM.300-301.344
• Online since: February 2013
• Authors: Zhou Wan, Xing Zhi Liao, Xin Xiong, Jin Chuan Han
• Keywords: EEMD, Fault Diagnosis, Improved Hilbert-Huang Transformation, Local Hilbert Marginal Spectrum, Signal Pretreatment

References

[1] Lingli Bu, Jianying Guo, Fenglin Jiang. Application of wavelet analysis and Hilbert analysis to rolling bearing fault diagnosis[J]. Journal of Harbin University of Science and Technology, 2008, 13(2): 82-85.

[2] Huang N E, Shen Z, Long S R. The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis [J]. Proceeding of Royal Society London, series A, 1998; 454: 903-995.

DOI: 10.1098/rspa.1998.0193

[3] Huang N E, Shen Z, Long S R . A new view of nonlinear water waves [J]. The Hilbert spectrum. Annual Review of Fluid Mechanics, 1999; 31: 417-457.

DOI: 10.1146/annurev.fluid.31.1.417

[4] Wu Z, Huang N E. Ensemble empirical mode decomposition: a noise-assisted data analysis method [J]. Advances in Adaptive Data Analysis, 2009; 1(1): 1-4l.

DOI: 10.1142/s1793536909000047

[5] LEI yaguo. Machinery fault diagnosis based on improved Hilbert-Huang Transform [J]. Chinese Journal of Mechanical Engineering, 2011, 47(5): 71-77.

DOI: 10.3901/jme.2011.05.071

[6] Dejie Yu, Junsheng Cheng, Yu Yang. Application of EMD method and Hilbert spectrum to the fault diagnosis of roller bearings [J]. Mechanical Systems and Signal Processing , 2005; 19: 259270.

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

[7] Youming Zhong, Shuren Qing, Baoping Tang. Study on the marginal spectrum in Hilbert-Huang transform[J]. Systems Engineering and Electronics, 2004; 26(9): 1324-1326.