Fault diagnosis of rolling bearing based on lifting morphological wavelet and ensemble empirical mode decomposition

Aiming at the fault diagnosis of rolling bearing in the case of complicated background, lifting morphological wavelet is used to denoise, and a method for extracting fault features is represented by combining lifting morphological wavelet with ensemble empirical mode decomposition (EEMD). The original signal is denoised firstly by max-lifting morphological wavelet and min-lifting morphological wavelet filter in this method, then fault feature information is extracted by obtained intrinsic mode function (IMF) after the denoised signal is decomposed using EEMD. The analysis results on bearing fault vibration test signal show that this method can extract fault features and identify fault types of bearing effectively.