CEEMD和小波半软阈值相结合的滚动轴承降噪

针对滚动轴承振动信号降噪处理时如何保证信号边缘信息完整性的问题,提出将互补集合经验模态分解(complementary ensemble empirical mode decomposition,简称CEEMD)与小波半软阈值相结合的信号降噪方法,对滚动轴承故障高频振动信号进行降噪处理。首先,采用CEEMD方法对故障振动信号进行分解,针对信号特点自适应获取不同频段模态分量;其次,将对包含噪声污染的高频信号模态分量进行相关性分析,得到含噪成分较高的高频模态分量,进一步采用小波半软阈值进行降噪处理;最后,将降噪后的模态分量同残余分量进行信号重构,完成降噪过程。分析结果表明,相对于传统小波阈值降噪和CEEMD强制降噪方法,提出的方法能够有效去除高频信号的噪声,且最大程度地保证了原始信号的完整性,降噪效果更好。

De-noising Method for Bearing Vibration Signal Based on CEEMD and Wavelet Semi-soft Threshold

Aiming at how to ensure the integrity of the antifriction bearing fault vibration signal while its noise reduction, a signal de-noising method is proposed using complementary ensemble empirical mode decomposition (CEEMD) combined with wavelet semi-soft threshold, to make the noise reduction of vibration signal of antifriction bearing. First of all, CEEMD signal processing method is used for antifriction bearing fault vibration signal decomposition and different frequency bands of intrinsic mode components (IMFs) can be adaptively obtained according to the signal characteristics. Secondly, the correlation analysis of high frequency signal components with noise pollution is executed, so the high frequency IMFs with higher noise components are achieved and then be handled by wavelet semi-soft threshold noise reduction processing. Finally, the IMFs are recombined after noise reduction processing and the remaining components of signal reconstruction, completing the noise reduction process. Analysis results show that compared with the traditional wavelet threshold de-noising processing method and CEEMD signal de-noising processing method, the proposed method of noise reduction in this paper based on CEEMD and semi-soft threshold wavelet is better, and it can ensure the integrity of the signal.