基于小波包近似熵和VPRS的轴承故障诊断

Bearing Fault Diagnosis Based on Wavelet Packet Approximate Entropy and VPRS

将变精度粗糙集(VPRS)引入到轴承故障诊断中,提出了基于小波包近似熵和VPRS的轴承故障诊断方法。先利用小波包近似熵对原始信号进行特征提取,再用VPRS理论对得到的特征表进行数据挖掘,提取规则集进行诊断。最后通过实例验证了此方法的有效性。

Variable precision rough set(VPRS) was introduced into bearing fault diagnosis,and a method of bearing fault diagnosis based on wavelet packet approximate entropy and VPRS was proposed. Firstly,the wavelet packet approximate entropy was used to extract the original signal features,and then the VPRS theory was used to perform data mining on the obtained feature table and extract the rule set for diagnosis.Finally,example verified the effectiveness of this method.