基于全矢小波包能量熵的滚动轴承智能诊断

研究滚动轴承不同状态下的振动信号,使用小波包变换提取信号各频带的能量熵,作为轴承故障的特征,然后使用支持向量机智能诊断轴承不同故障。传统单通道信号诊断方法容易造成误诊,全矢小波包能量熵融合了振动信号双通道的信息,能更准确地反映故障的特征。实验结果表明,采用全矢小波包能量熵比传统单通道方法有更高的诊断精度。

Intelligent diagnosis of rolling bearing based on full vector wavelet packet energy entropy

Research on the vibration signal of rolling bearing under different state,using wavelet packet transform to extract energy entropy of each frequency band of signal as the feature of bearing fault,then using support vector machine to diagnose different faults of bearing intelligently.It is easy to be misdiagnosed with the traditional single channel signal diagnostic method.Full vector wavelet packet energy entropy integrates two-channel information of vibration signal.It can reflect the characteristics of fault more accurately.Test results show that,full vector wavelet packet energy entropy has a higher diagnosis accuracy than the traditional single channel method.