Fault feature extraction of low speed roller bearing based on Teager energy operator and CEEMD

Abstract The fault signals of low-speed rolling elements bearing are non-stationary and non-linear, and consequently it is difficult to extract the fault characteristics by the traditional time and frequency domains analysis methods. Furthermore, the vibration signals suffer from severe signal attenuation and noise corruption during the signal transmission process. In order to effectively enhance and extract the fault characteristics from weak bearing signal, it requires effective signal processing strategies or high sensitive sensors to detect the low energy bearing vibration signals. In this paper, one such signal processing method is proposed to detect fault characteristics combined Teager energy operator and Complementary Ensemble Empirical Mode Decomposition (CEEMD). In this method, firstly Teager energy operator is used to strengthen the signal after wavelet noise reduction since it has good temporal resolution and adaptive ability for signal transient changes, and has unique advantages in detecting signal impact characteristics. Then CEEMD algorithm is carried out to extract bearing fault through Intrinsic Mode Function (IMF) decomposition. The proposed method is validated by a scaling model test rig of a wind turbine. The results validate that the method can effectively extract the fault characteristics of low-speed bearings and identify the bearing fault.