形态滤波与EEMD在振动筛轴承故障诊断中的应用  

Application of Morphology Filter and EEMD in Fault Diagnosis for Vibrating Screen Bearings

作　　者：徐元博[1] 魏振东[2] 

机构地区：[1]西京学院机电技术系,西安710123 [2]华中科技大学机械科学与工程学院,武汉430074

出　　处：《轴承》2015年第10期41-44,共4页Bearing

基　　金：陕西省民办高等教育发展专项资金;西京学院科研基金项目(XJ130245;XJ130244)

摘　　要：轴承故障诊断的关键在于振动信号的前处理与故障特征参数的提取。形态滤波法可以利用特有的数学属性对故障信号进行有效的降噪处理,并突出故障信号的特征参数;同时利用集合平均经验分解对轴承的特有故障特征进行提取。将该振动信号提取方法应用到振动筛等振动机械的轴承故障特征提取中,通过试验表明,该方法可以有效提取振动机械中故障信号的频谱特征,便于工程应用。The key of fault diagnosis for bearings is the preprocessing of vibration signal and extracting of fault characteristic parameters. The morphology filtering algorithm is effectively used for denoising treatment of fault signals according to specific mathematical properties,and the characteristic parameters are highlighted for fault signals. The specific fault characteristics of bearings are extracted by using ensemble average empirical mode decomposition. The algorithm is applied to extracting fault characteristics of vibrating screen bearings. The experiments show that the algorithm can effectively extract spectrum feature of fault signals in vibrating machine,which is convenient to be used in engineering field.

关 键 词：滚动轴承 振动筛 故障诊断 形态滤波法 集合平均经验分解 

分 类 号：TH133.33[机械工程—机械制造及自动化] TN911.7[电子电信—通信与信息系统]