摘要
文中提出一种小波包能量熵和概率神经网络相结合的算法,通过对微特电机运行产生的振动信号进行分析,实现微特电机的轴承可靠性判定。为了证明该方法的有效性,搭建了电机振动信号采集和分析的软硬件平台。通过对4种不同故障类型的电机进行实验,结果表明该方法可以作为微特电机轴承可靠性判定的依据。
A combination algorithm of wavelet packet energy entropy and probabilistic neural network was present. Bymeans of analyzing the vibration signals of micromotor, the bearing reliability can be predicated. In order to prove the effectiveness of the method, the software and hardware platform for the motor vibration signal acquisition and analysis was set.Through the experiment of four different motor fault types, the results show that the algorithm can be used as a basis of reliability judgment of micromotor bearing.
出处
《微特电机》
北大核心
2016年第4期37-39,51,共4页
Small & Special Electrical Machines
关键词
小波包熵
概率神经网络
微特电机
轴承
监控
LABVIEW
wavelet package entropy
probabilistic neural network
micromotor
bearing
monitoring
LabVIEW
