Rolling bearing fault extraction method based on CEEMDAN and multi-parameter feature screening

To solve the problem of extracting fault information from high-frequency vibration signals of aero-engine rolling bearing fault excitation, based on the analysis and processing of vibration signals, a fault feature recognition method combining Complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN), kurtosis and multi-scale permutation entropy normalized screening index, spectrum analysis, and envelope demodulation was proposed. Firstly, CEEMDAN was used to decompose the signal to obtain several sub signals. The multi-parameter screening index was used to normalize the sub signals, and the five sub signals with larger values were selected for reconstruction. Finally, envelope demodulation and envelope spectrum analysis and processing were used to extract the fault characteristics of rolling bearings. This method was used to analyze and diagnose the typical fault data of deep groove ball bearings from Case Western Reserve University and the in-house experimental data of rolling bearings. The results show that this method can effectively improve the high-frequency resolution of fault signals, retain periodic impact components, and realize accurate identification and fault diagnosis of rolling bearing fault characteristics.