Research on Fault Diagnosis for Roller Bearings Based on MED and Slice Bi-Spectrum

Abstract

The rolling bearings fault feature under strong background noise is very weak for reasons of environment noise impact and signal attenuation. To extract the fault features of roller bearings effectively, a new method based on Minimum entropy deconvolution (MED) and slice bi-spectrum is proposed. The paper Firstly decreases the strong background noise of rolling bearing by the MED method, and then calculates the envelope signal of the de-noised signal. Finally, analyzes the envelope signal with slice bi-spectrum and extracted the fault characteristic frequency. The effectiveness of the proposed method was validated by analysis of both a simulated faulty bearing vibration signal and the experiment measured signal of rolling bearing, and it was also compared with the method of envelope spectrum.