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

Wavelet thresholding as a common signal processing method for fault diagnosis of rolling bearing has the deficiency of difficulty to choose basic function and the weakness of poor denoising performance by using conventional soft or hard threshold. A method combining EMD interval-thresholding with maximum likelihood estimation to diagnose the incipient weak fault of rolling bearing was presented. The original signal was analyzed by empirical mode decomposition (EMD), then each intrinsic modal function (IMF) was denoised by interval-thresholding based on maximum likelihood estimation and the fault signals was acquired by reconstructing the thresholded IMFs. Finally, the results were achieved by envelope spectrum analysis 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.