基于最优Morlet小波的滚动轴承故障诊断  

Bearing Fault Diagnosis Based on Optimal Morlet Wavelet

作　　者：张丹[1] 隋文涛[2] 张宇[2] 

机构地区：[1]山东理工大学电气与电子工程学院,山东淄博255049 [2]山东理工大学机械工程学院,山东淄博255049

出　　处：《轴承》2009年第10期48-51,共4页Bearing

基　　金：山东省自然科学基金资助项目(Y2005F12)

摘　　要：将最优Morlet小波和阈值降噪法相结合,进行强噪声背景下滚动轴承故障诊断。依据峭度最大准则确定最优Morlet小波基。利用连续小波变换和软阈值法对振动信号降噪。试验表明,该方法具有良好的去噪性能,并能更好地提取滚动轴承振动信号中的故障特征。A fault diagnosis method based on Morlet wavelet and thresholding denoising is proposed to diagnose rolling bearing fault in high noise condition. The optimal Morlet wavelet base function is determined according to kurtosia maxi- mization criteria. The continuous wavelet transformation and soft threshold methods are used to denoise vibration sig- nals. Experiments show that the proposed method is valid for de - noising, and it can effectively extract fault feature from vibration signals.

关 键 词：滚动轴承 故障诊断 MORLET小波 降噪 

分 类 号：TH133.33[机械工程—机械制造及自动化] TP306[自动化与计算机技术—计算机系统结构]