基于 EEMD 和改进的形态滤波方法的轴承故障诊断研究

Localized defects in bearings tend to arouse periodical impulsive vibration, and bearing fault diagnosis can be realized by detecting and extracting impulsive response components. However, under the practical environment, the fault-related impacts are usually overwhelmed by noise. Based on analysis of ensemble empirical mode decomposition (EEMD) and morphological filtering, a hybrid method combining EEMD method and an improved morphological filtering was proposed. A new structural element decision strategy was proposed to analyze intrinsic mode functions (IMFs) and extract periodical impulsive signal features. The performance of the proposed method was validated by detecting vibration signals of defective rolling bearings with outer and inner circle faults. The results showed that the proposed method is effective for extracting periodic impulses and suppressing noise of vibration signals.