Rolling Bearing Fault Diagnosis Based on CEEMDAN and Refined Composite Multiscale Fuzzy Entropy
Shuzhi Gao, Quan Wang, Yimin Zhang
Abstract
Considering the nonlinear and nonstationary characteristics of rolling bearing vibration signals, we propose a rolling bearing fault diagnosis method based on complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN), refined composite multiscale fuzzy entropy (RCMFE), Laplace score (LS), and the particle swarm optimization-probabilistic neural network (PSO-PNN). First, the method employs CEEMDAN to decompose the vibration signal and select the intrinsic mode functions (IMFs) containing the primary fault information via the frequency-domain correlation coefficient method. Then, it uses RCMFE to extract the characteristic information from the selected IMF. In addition, it uses LS to select and construct low-dimensional sensitive feature vectors, which are incorporated into the PSO-PNN model for diagnostic analysis to realize the state recognition of rolling bearing. Finally, the effectiveness of the method is verified by the analysis of the experimental data.