摘要
滚动轴承是易损件,为了更好并及时检测出在信噪比低的情况下的轴承早期微故障振动信号,提出了小波包最优熵和EEMD相结合的方法。运用小波包最优熵对采集信号实现信噪分离,突出了小波包降噪效果明显;通过EEMD将信号分解成多个分量;最后以互相关、峭度准则提取故障信号分量以避免分量选择的盲目性。结果表明:该方法对轴承初期故障具有良好的降噪效果,可以准确快速地检测出轴承故障,从而表明该方法是有效且可行的。
Rolling bearing is the easy wearing part. In order to better and timely detect early and slight bearing fault vibration sig- nal under low signal-noise ratio, the method of wavelet packet optimal entropy combined with EEMD was proposed. Signal-noise separa- tion was achieved by wavelet packet optimal entropy, which highlighted the wavelet packet noise reduction effect was obvious, through EEMD decomposed the signal into a plurality of component. Finally, cross-correlation, kurtosis criterion were used to extract fault sig- nal component in order to avoid the blindness of component selection. The results show that ： the method of bearing early fault has good noise reduction effect, which can accurately and quickly detect bearing fault, thereby to show that the method is feasible and effective.
出处
《机床与液压》
北大核心
2015年第3期189-193,共5页
Machine Tool & Hydraulics
基金
内蒙古自治区自然科学基金项目(2012MS0717)
关键词
小波包最优熵
EEMD
互相关
峭度
轴承故障
Wavelet packet optimal entropy
EEMD
Cross-correlation
Kurtosis
Bearing fault
