Gear Fault Diagnoise Based on Ensemble Empirical Mode Decomposition and Instantaneous Energy Density Spectrum

Jin Ming Lu; Fan Lin Meng; Hua Shen; Li Bing Ding; Su Nin Bao

doi:10.4028/www.scientific.net/AMM.142.3

Paper Titles

Gear Fault Diagnoise Based on Ensemble Empirical Mode Decomposition and Instantaneous Energy Density Spectrum
p.3

Numerical Analysis of Nonlinear Behaviors of a Flexible Rotor Dynamic System with Turbulent Journal Bearings Support
p.7

Dynamic Path Planning Algorithm Based on Chaos Genetic Vehicle Navigation
p.12

The Hybrid Genetic Algorithm of Single-Machine Materials Manufacturing Process with Periodic Maintenance
p.16

The Improved Design of Some Shrunk Super High Pressure Cylinder Based on Equal Strength Theory
p.20

Design of Ultra-High Pressure Cylinders Based on Principle of Autofrettage
p.24

The High Resolution Lunar Planet DEM Based on the Altimetry Data of 400-Circle CE-1 Laser Altimeter (2B)
p.28

Effect of Ammonium and Nitrate Ratios on Growth and Yield of Chinese Kale
p.32

HomeApplied Mechanics and MaterialsApplied Mechanics and Materials Vol. 142Gear Fault Diagnoise Based on Ensemble Empirical...

Gear Fault Diagnoise Based on Ensemble Empirical Mode Decomposition and Instantaneous Energy Density Spectrum

Abstract:

A very short impulse energy called ‘impulsion energy’ can be produced when the gear meshing with gear pitting fault and excited the resonance of the structure. The common techniques have inconvenience to deal with this vibration signal. A new fault diagnosis method based on EEMD and instantaneous energy density spectrum is proposed here. The IMFs generated by EEMD can alleviate the problem of mode mixing and approach the reality IMFs. The characteristic frequencies were found in the instantaneous energy density of Hilbert spectrum. The effectiveness of this method was demonstrated by analysis the vibration signals of a gear with pitting fault.

You might also be interested in these eBooks

Intelligent Materials, Applied Mechanics and Design Science

View Preview

Info:

Periodical:

Applied Mechanics and Materials (Volume 142)

Pages:

3-6

DOI:

https://doi.org/10.4028/www.scientific.net/AMM.142.3

Citation:

Cite this paper

Online since:

November 2011

Authors:

Jin Ming Lu, Fan Lin Meng, Hua Shen, Li Bing Ding, Su Nin Bao

Keywords:

Ensemble Empirical Mode Decomposition (EEMD), Fault Diagnosis, Gear, Instantaneous Energy Density, Intrinsic Mode Function (IMF)

Export:

RIS, BibTeX

Price:

Permissions CCC:

Request Permissions

Permissions PLS:

Request Permissions

Сopyright:

Citation:

References

[1] D.J. YU, J.S. CHENG. Application of empirical mode decomposition method to gear fault diagnosis. Journal of Hunan University(Natural Sciences Edition). 2002, Vo1. 29, No. 6.

Google Scholar

[2] B. Liu,S. Riemenschneidera,Y. Xu. Gearbox fault diagnosis using empirical mode decomposition and Hilbert spectrum. Mechanical Systems and Signal Processing 20 (2006) 718–734.

DOI: 10.1016/j.ymssp.2005.02.003

Google Scholar

[3] N.E. Huang, Z. Shen, S.R. Long. The empirical mode de-composition and the Hilbert spectrum for nonlinear and non-stationary time series analysis[J]. In: Proc. R. Soc. Lond. A, 1998; 454: 903-995.

DOI: 10.1098/rspa.1998.0193

Google Scholar

[4] Khalid F. Al-Raheem, Asok Roy, K. P. Ramachandran, D. K. Harrison, and Steven Grainger2. Rolling Element Bearing Fault Diagnosis Using Laplace-Wavelet Envelope Power Spectrum. EURASIP Journal on Advances in Signal Processing. Volume 2007, Article ID 73629, 14 pages.

DOI: 10.1155/2007/73629

Google Scholar

[5] Yaguo Lei and Ming J Zuo. Fault diagnosis of rotating machinery using an improved HHT based on EEMD and sensitive IMFs. Measurement Science and Technology. 2009, 20, 125701.

DOI: 10.1088/0957-0233/20/12/125701

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. 2009, VOl. 1, No. 1, 1-41.

DOI: 10.1142/s1793536909000047

Google Scholar

[7] Z.H. WU. The multi-dimensional ensemble empirical mode decomposition method. Advance in Adaptive Data Analysis. 2009, Vol 1, No. 3 339-372.

Google Scholar