基于小波包和分形盒维数的滚动轴承故障诊断  

Application of wavelet packet and fractal box-counting dimension in fault diagnosis of rolling bearing

作　　者：李曙光[1] 张梅军[1] 陈江海[1] 

机构地区：[1]解放军理工大学工程兵工程学院,江苏南京210007

出　　处：《机械》2010年第8期21-23,36,共4页Machinery

摘　　要：为诊断滚动轴承不同部件产生的故障,针对轴承故障信号具有非线性、非平稳振动的特点,运用小波包和分形理论,定量计算了滚动轴承不同部件故障信号及小波包重构信号的盒维数。实验结果表明,滚动轴承不同的故障类型具有不同的盒维数。正常滚动轴承盒维数最大,依次为滚珠故障盒维数、内环故障盒维数,外环故障盒维数最小。分形盒维数能定量地识别滚动轴承不同部件的故障,提高滚动轴承故障诊断的准确率,为滚动轴承智能故障诊断提供可靠依据。To diagnose the fault types of rolling bearings,box-counting dimensions of non-linear unstable vibration signals and wavelet package reconstruction signals of rolling bearing components are calculated quantitatively based on the fractal theory..The experiment results show that the box-counting dimensions of various fault types are different evidently.The box-counting dimension of normal rolling bearing is the largest.The box-counting dimension of outer ring fault is the smallest.The fractal box-counting dimension can identify various fault types and promote the accuracy of rolling bearing components.Also,the fractal box-counting dimension provides reliable grounds for intelligent fault diagnosis.

关 键 词：小波包 分形 盒维数 滚动轴承 故障诊断 

分 类 号：TH133.33[机械工程—机械制造及自动化] TH165.3