基于EEMD和改进的形态滤波方法的轴承故障诊断研究

轴承故障会导致振动信号中出现冲击响应成分,可通过对冲击响应成分的周期的检测与提取, 进行局部故障诊断。但在复杂工况下，故障脉冲易被周围噪声淹没，在分析EEMD和形态学滤波方法的基础上，将EEMD方法与形态学滤波方法相结合，提出结构元素(SE)选择方法，并用于本征模态信号中冲击响应特征的提取。通过将该方法用于轴承外圈、内圈局部故障状态下的特征的检测，结果表明该方法能有效提取周期性脉冲成分并抑制噪声。

Rolling element bearing fault diagnosis based on EEMD and improved morphological filtering method

Localized defects in bearings tend to arouse periodical impulsive vibration, and the diagnosis of the bearing can be realized by detecting and extracting the impulsive components. However, under the practical environment, the fault related impacts are usually overwhelmed by the noise. Based on the analysis of Ensemble empirical mode decomposition (EEMD) and morphological filtering, a hybrid method which combines the EEMD method and an improved morphological filtering is proposed. A new structure element decision strategy is proposed to analysis the Intrinsic Mode Function (IMF) to extract the periodical impulsive signal feature extraction. The performance of the proposed method is validated by vibration signals of defective rolling bearing with outer and inner faults. The result shows that the proposed method is effective in extracting periodic impulses and suppressing the noises of vibration signals.