提升小波包在滚动轴承故障特征提取中的应用

为从滚动轴承振动信号中提取出故障状态信息的特征，针对信号的特点和提升小波包变换性质，采用提升小渡包最优分解法获得故障敏感特征频带，对各频带进行标准化向量特征构造，提取出各个频带的故障特征。结果表明，滚动轴承故障信号的敏感特征频带能量集中明显，故障特征得以有效的提取出来。

Feature Extraction of Ball Bearing Fault Based on Lifting Wavelet Packet Transform

For extracting the characteristics from ball bearing vibration signals which are in fault state, the lifting wavelet packet decomposition method is adopted to obtain the fault sensitive feature frequency band according to nature of the signal and characteristics of lifting wavelet packet transformation. After that, the fault features of each frequency band were extracted successfully after standardizing them. Furthermore, in an example analysis, the sensitive feature frequency band of ball bearing fault signal has obvious energy concentration, and the fault features has been extracted effectively.