Feature extraction based on vibration signal decomposition for fault diagnosis of rolling bearings

Rolling bearings typically operate under time-varying conditions, which present challenges for fault diagnosis due to the presence of modulation effect and noise component in the bearing vibration signal. This paper introduces a novel feature extraction method that combines complementary ensemble empirical mode decomposition (CEEMD), Teager-Kaiser energy operator (TKEO), and self-organizing map (SOM) to enhance the identification accuracy of bearing characteristics by removing unwanted components from the vibration signal. Firstly, CEEMD and TKEO are used to generate demodulated instantaneous energies of the raw vibration signal and each decomposed mode, which represent bearing features. Next, relevant features are extracted by computing similarity distances using the SOM network. The feature signal is then reconstructed, and its power spectrum is used to evaluate the visualization of the characteristic frequencies of rolling bearings. The efficiency of the proposed method is confirmed through the analysis of simulated and experimental vibration signals collected from two types of bearings with different geometries. The results demonstrate that the proposed method can effectively suppress noise, extract characteristics, and identify the fault condition of rolling bearings compared to previous studies.