摘要
滚动轴承是电机中较为薄弱的环节,其运行状态是否正常往往直接影响到整台机器的性能,其故障早期不宜被人发现,若发现不及时引起系统瘫痪或电机损坏,损失惨重,因此对滚动轴承的故障检测要求十分迫切。本文在Y160M2-8型电机轴承故障实验台进行实验,模拟了轴承故障时的振动信号,运用小波包多重分解重构理论对滚动轴承原始信号进行初步处理消除噪声影响,利用分形理论对消噪后的振动信号进行关联维数分解,是一种有效的求取相应特征量实现故障诊断的方法。
Motor bearing is a relatively weak link, and its operational status often directly affects the performance of the entire machine. It is hard to find their fault earlier. If it is not found in time it can not only damage motors itself but also affect the normal work of the whole production system and even will result in huge economic loss. Thus it is urgent for diagnosis of motor bearing fault. This paper experimented in Y160M2-8 type motor bearing fault test and simulated the fault vibration signal of rolling bearing. The method deletes noise by wavelet-packet decomposition and reconstruction for original signal at first. Then uses Fractal theory to decompose the signal got by deleting noise and realize correlation dimension representing fault characteristics. The method can extract fault characteristic quantities effectively and easy to judge fault type.
出处
《电气技术》
2011年第1期21-23,共3页
Electrical Engineering
关键词
小波包
关联维数
滚动轴承
wavelet-packet
correlation dimension
rolling bearing
