Fault diagnosis of rolling bearings based onEMD Interval-Threshold denoising and maximum likelihood estimation

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Journal of Vibration and Shock ›› 2013, Vol. 32 ›› Issue (9) : 155-159.

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Fault diagnosis of rolling bearings based onEMD Interval-Threshold denoising and maximum likelihood estimation

ZHOU Zhi, ZHANG You-yun, ZHU Yong-sheng，YUAN Xing, SHI Hao-hao

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Abstract

Wavelet thresholding as a common signal processing for fault diagnosis of rolling bearing, but have the deficiency of difficulty to choose basic function and poor performance of conventional soft and hard threshold. EMD is an adaptive decomposition method based on signal completely, a method combined EMD Interval- Thresholding with maximum likelihood estimation to diagnose the incipient weak fault of rolling bearing is presented in this paper. Firstly, the original signal is analyzed by empirical mode decomposition, then each IMF is denoised by Interval-Thresholding based on maximum likelihood estimation and the fault signals is acquired by reconstructing the thresholded IMFs, finally the results are achieved by spectrum of denoised signal. The results of numerical simulation and an industrial case show that the proposed method is effective to diagnose the fault of rolling bearing significantly.

Key words

EMD Interval-Thresholding / Maximum Likelihood Estimation / Fault diagnosis / Rolling bearing

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ZHOU Zhi;ZHANG You-yun;ZHU Yong-sheng;YUAN Xing;SHI Hao-hao. Fault diagnosis of rolling bearings based onEMD Interval-Threshold denoising and maximum likelihood estimation[J]. Journal of Vibration and Shock, 2013, 32(9): 155-159