Rolling Bearing Fault Feature Extraction Based on ALIF and FWEO

Aiming at the problem that the traditional algorithm is difficult to accurately extract the weak fault characteristics of rolling bearings under strong background noise, A fault feature extraction method based on ALIF (adaptive local iterative filtering) and FWEO (frequency weighted energy operator) is proposed. Using ALIF to decompose the fault signal into several I components, by calculating the kurtosis values of different I components and the correlation coefficient with the original signal, the two I components with the greatest correlation with the original signal are filtered and reconstructed, and the solution is solved by FWEO. Finally, the energy spectrum of the reconstructed signal is obtained to extract the fault characteristic information of the bearing. At the same time, compared with the methods based on classical algorithms EMD and FWEO, the simulated and experimental results show that the method is more effective in fault information extraction and can achieve more effective diagnosis of rolling bearing faults.