Fault diagnosis of rolling bearing based on wavelet package transform and ensemble empirical mode decomposition

Abstract

Rolling bearing is widely used in rotating mechanical system, and its operating state has great influence on accuracy, reliability, and the life of the whole mechanical system. Therefore, fault diagnosis of rolling bearing is indispensible for the health monitoring in rotating machinery system. Wavelet package transform (WPT) and envelope demodulation have been common methods in diagnosis of bearing fault, but the precision of diagnostic results is limited by the degree of damages on bearing. In this paper, a method based on WPT and ensemble empirical mode decomposition (EEMD) is proposed to detect the fault of rolling bearing and solve this problem. According to simulation and experimental results, it is effective in fault diagnosis of rolling bearing and is better than the method based on WPT and envelope spectrum while the faults get more serious. © 2013 Quan Liu et al.