基于小波包与CEEMDAN的MC主轴振动信号降噪方法

针对采集到的加工中心(Machining Center, MC)主轴振动信号中包含大量噪声,导致无法准确地识别MC主轴故障的问题,提出能对高低频的噪声都能分析处理的小波包与具有自适应噪声的完整集成经验模态分解(Complete Ensemble Empirical Mode Decomposition with Adaptive Noise,CEEMDAN)相结合的降噪方法,对MC主轴振动信号进行降噪处理。使用小波包算法对主轴振动信号进行预处理,通过CEEMDAN方法对预处理的主轴振动信号进行分解,得到多个本征模态函数(Intrinsic Mode Function,IMF)分量和残余分量;计算预处理的主轴振动信号与IMF之间的相关系数,并对高相关系数的IMF分量进行阈值降噪处理;把处理后的IMF分量、未处理的IMF分量和残余分量叠加以获得重建信号。最后,采用仿真信号和真实MC主轴振动信号进行测试,证明该降噪方法能有效去除噪声,使得信号特征的可分离性更高,提高了MC主轴故障识别能力。

De-noising Method of MC Spindle Vibration Signal Based on Wavelet Packet and CEEMDAN

Aiming at the large amount of noise in the vibration signal of the collected Machining Center spindle,it is impossible to accurately identify the problem of the spindle fault.It is propose a de-noise method that combined the wavelet packet with high and low frequency noise can be analyzed and the complete ensemble empirical mode decomposition with adaptive noise(CEEMDAN),the vibration signal of the MC spindle is de-noised.The wavelet packet algorithm is used to preprocess the spindle vibration signal,and the pre-processed spindle vibration signal is decomposed by CEEMDAN method to obtain multiple Intrinsic Mode Function(IMF)components and residual components.Calculating a correlation coefficient between the pre-processed spindle vibration signal and the IMF,and performing threshold de-noise processing on the IMF component of the high correlation coefficient;superimposing the processed IMF component,the unprocessed IMF component,and the residual component to obtain a reconstructed signal.Finally,the simulation signal and the actual MC spindle vibration signal are used to test,which proves that the noise reduction method can effectively remove noise,make the signal feature separability higher,and improve the MC spindle fault recognition ability.