Early Bearing Fault Detection Using EEMD and Three-Sigma Rule Denoising Method

Abstract

Rotating electrical machines have several physical phenomena. Vibration is one of the important phenomena in the operation of rotating electrical machines. In addition, the vibration signal is considered an important source to have good information on the state of rotating electrical machinery. But this signal is rich in noise, especially under the presence of the bearing fault. This paper proposes a bearing fault diagnosis method based on EEMD and a denoising method based on three-sigma rule. In the first step, the EEMD decomposed the vibration signal into several components called Intrinsic Mode Functions (IMFs). After the calculation of the kurtosis of each IMF component, the signal is reconstructed by choosing components with higher values. To enhance periodic impulses, the three-sigma rule de-noising is applied to the reconstructed signal. As a final step, the envelope spectrum is used to determine the fault characteristic frequency. As a result of testing the bearing with inner race fault and the bearing with outer race, it was verified that the proposed approach suppressed noise effectively and extracted rich fault information from the vibration signals of bearings compared to the EEMD.