LAUSR

As one of the most important components in rotating machinery, rolling bearings are fragile due to their harsh working environment. The empirical wavelet transform (EWT) method has been used to identify bearing faults by constructing an adaptive filter bank and decomposing the vibration signal into different modes. However, the EWT method separates different parts based on the local extremum of the Fourier spectrum. This segmentation strategy is highly susceptible to other components such as gears, and sometimes the fault characteristics of bearings cannot be detected. To overcome the above drawbacks, frequency phase space empirical wavelet transform (FPSEWT) method is proposed in this paper, which divides the spectrum into several parts and uses the Teager energy distribution as a reference for dividing the Fourier spectrum. Then, the differential search (DS) algorithm is applied to automatically identify the optimal right and left boundaries of the sensitive frequency band. The effectiveness of the proposed approach is verified by both simulated signals and experimental data. The fault feature ratio (FFR) values of sensitive components increased from 8.08% to 18.67%, which indicate that the proposed method can successfully extract fault symptoms of rolling element bearings in the presence of environmental disturbance.