Sifting process of EMD and its application in rolling element bearing fault diagnosis

Among the vibration-based fault diagnosis methods for rolling element bearing, the shock pulse method (SPM) combined with the demodulation method is a useful quantitative technique for estimating bearing running state. However, direct demodulation often misestimates the shock value of characteristic defect frequency. To overcome this disadvantage, the vibration signal should be decomposed before demodulation. Empirical mode decomposition (EMD) can be an alternative for preprocess bearing fault signals. However, the trouble with this method’s application is that it is time-consuming. Therefore, a novel method that can improve the sifting process’s efficiency is proposed, in which only one time of cubic spline fitting is required in each sifting process. As a consequence, the time for EMD analysis can be evidently shortened and the decomposition results simultaneously maintained at a high precision. Simulations and experiments verify that the improved EMD method, combined with SPM and demodulation analysis, is efficient and accurate and can be effectively applied in engineering practice. This paper was recommended for publication in revised form by Associate Editor Eung-Soo Shin Hongbo Dong was born in Chaoyang, China, in 1979. He received the B.E. and M.E. degree from Northwestern Polytechnical University in Mechanical Engineering in 2002 and 2005 respectively and received the Ph.D degree from Xi’an Jiaotong University in Mechanical Engineering in 2009. His research interests include fault diagnosis of rotor and bearing system. Bing Li was born in Xuzhou, China, in 1976. He received the B.E. and M.E. degree from Northwestern Polytechnical University in Mechanical Engineering in 1999 and 2002 respectively and received the Ph.D degree from Xi’an Jiaotong University in Mechanical Engineering in 2005. After graduating from Xi’an Jiaotong University, he works as a lecturer in Xi’an Jiaotong University. His research interests include wavelet finite element theory and its application in fault diagnosis.