Research on Fault Diagnosis for Gear Based on Ensemble Empirical Mode Decomposition and Slice Bi-Spectrum

Abstract:

A new method on fault diagnosis for gear based on ensemble empirical mode decomposition and slice bi-spectrum is proposed. Firstly, fault signal was decomposed into a series of intrinsic mode function components of different frequency bands by EEMD, and then calculated the envelope signal of IMF component by Hilbert demodulation method. Finally, analyzed the envelope signal by slice bi-spectrum and extracted the fault characteristic frequency. The anti-alias decomposition capacity of EEMD and capabilities of noise suppression and non-quadratic phase coupling harmonic components elimination of slice bi-spectrum were verified by analyzing the simulation signal. The analysis results of gear pitting failure signal and gear wear fault signal showed that this method could judge gear fault type accurately and has a certainly degree reliability.

• Periodical: Advanced Materials Research (Volumes 718-720)
• Pages: 934-939
• DOI: https://doi.org/10.4028/www.scientific.net/AMR.718-720.934
• Online since: July 2013
• Authors: Gui Ji Tang, Xiao Long Wang
• Keywords: Ensemble Empirical Mode Decomposition, Fault Diagnosis, Gear, Slice Bi-Spectrum

References

[1] He Tian, Lin Yizhou, Gao Pugang, SHEN Yongjun, Application of local mean decomposition in gear fault diagnosis, Journal of Vibration and Shock. 30 (2011) 196-201.

[2] Yu Dejie, Cheng Junsheng, Cheng Qiong, Application of phase power spectrum based on complex wavelet transforms to fault diagnosis of gears, Journal of Vibration, Measurement & Diagnosis. 24 (2004) 275-276.

[3] Du Qiuhua, Li Chao, Wang Fei,1.5 dimensional spectrum analysis and its application to diagnosis of ball bearing defect, Journal of Vibration, Measurement & Diagnosis. 30 (2010) 315-317.

[4] Duan Xiangyang, Wang Yongsheng, Application of slice bi-spectrum analysis to fault diagnosis of centrifugal pump, Journal of Vibration, Measurement & Diagnosis. 30 (2010) 581-584.

[5] Zhang Chao, Chen Jianjun, Contrast of ensemble empirical mode decomposition and empirical mode decomposition in modal mixture, Journal of Vibration and Shock. 29 (2010) 87-90.

[6] Z.H. Wu, N.E. Huang, Ensemble empirical mode decomposition-a noise assisted data analysis method, Advances in Adaptive Data Analysis. 1 (2009) 1-41.

DOI: 10.1142/s1793536909000047

[7] C.L. Nikias, M.R. Raghuveer, Bi-spectrum estimation: a digital signal processing framework, Proceedings of IEEE. 75 (1987) 869-891.

DOI: 10.1109/proc.1987.13824

[8] A. Rivola, P.R. White, Detection of rolling element bearing damage by statistical vibration analysis, Journal of Sound and Vibration. 216 (1988) 889-910.

[9] Chen Lue, Zi Yanyang, Noise-assisted EMD-1.5 dimension spectrum for signal anti-alias decomposition and feature extraction, Journal of Vibration and Shock. 29 (2010) 26-30.