基于EEMD和改进的形态滤波方法的轴承故障诊断研究  

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

作　　者：沈长青[1] 谢伟达[2] 朱忠奎[3] 刘方[1] 黄伟国[3] 孔凡让[1] 

机构地区：[1]中国科学技术大学精密机械与精密仪器系,合肥230027 [2]香港城市大学系统工程与工程管理系 [3]苏州大学城市轨道交通学院,江苏苏州215021

出　　处：《振动与冲击》2013年第2期39-43,66,共6页Journal of Vibration and Shock

基　　金：国家自然科学基金资助项目(51075379);江苏省自然科学基金资助项目(BK2010225)

摘　　要：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。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

关 键 词：轴承 故障诊断 整体平均经验模态分解 滤波 数学形态学 

分 类 号：TH165.3[机械工程—机械制造及自动化]