基于广义形态学滤波和Hilbert边际谱的滚动轴承故障诊断  

Fault Diagnosis of Rolling Bearings Based on Generalized Morphological Filter and Hilbert Marginal Spectrum

作　　者：崔宝珍[1] 马泽玮 李会龙[1] 王珊[1] 

机构地区：[1]中北大学,太原030051 [2]运城学院,运城044000

出　　处：《中国机械工程》2016年第11期1484-1488,共5页China Mechanical Engineering

基　　金：国家自然科学基金资助项目(50875247);山西省自然科学基金资助项目(2009011026-1);山西省研究生创新基金资助项目(2008072)

摘　　要：广义形态滤波器可以很好地抑制输出统计偏倚的现象,Hilbert边际谱克服了传统包络法需要确定带通滤波器的中心频率和带宽的不足,将两种方法相结合,首先利用广义形态滤波对信号进行去噪,在此基础上对信号进行经验模态分解,然后选取合适的IMF分量得到信号的局部Hilbert边际谱。通过对轴承内外环进行故障诊断发现,该方法能准确地提取故障特征,从而有效地判别轴承的故障类型和部位,具有较广阔的应用前景。Generalized morphological filter output could be good at eliminating the phenomenon of statistical bias,Hilbert marginal spectrum envelope method overcame the traditional needs to identify deficiencies bandpass filter center frequency and bandwidth.Combining the two methods mentioned,generalized morphological filter was used to complete the signal de-noising,then decomposing signals by EMD and then selecting the appropriate IMF components the partial Hilbert marginal spectrum of the signals was obtained.The bearing inner and outer ring fault diagnosis results show that the method may accurately extract fault features,which determines the type and location of bearing failure effectively,so it has wider applications in many fields.

关 键 词：滚动轴承 广义形态滤波 经验模态分解 Hilbert边际谱 

分 类 号：TH165.3[机械工程—机械制造及自动化]