Fault signal analysis of rolling bearing based on CEEMDAN decomposition method

Abstract

The running of rolling bearings plays a key role in the safety and stability of the whole system. In this paper, an improved Hilbert-Huang Transform (HHT) time-frequency analysis method is proposed to analyze and diagnose the fault signals of rolling bearings. At first, the fault signals of rolling bearings are analyzed and compared by three time-frequency analysis methods: Short-Time Fourier Transform (STFT), Continuous Wavelet Transform (CWT) and Hilbert-Huang transform (HHT). At the same time, to solve the problems of modal aliasing in empirical mode decomposition (EMD) in HHT, the signal is decomposed by complete EEMD with adaptive noise (CEEMDAN), and the results are compared according to Permutation Entropy (PE). The results show that the HHT time-frequency analysis method is more adaptive than other methods, and the CEEMDAN decomposition method is more accurate in fault signal analysis of rolling bearings.