基于WTD和CEEMD的轴承故障特征提取方法

针对轴承故障信号常混有噪声干扰且故障特征难以准确提取问题，提出一种基于小波阈值去噪(WTD)和互补集合经验模态分解(CEEMD)的轴承故障特征提取方法。采用WTD对原始信号进行降噪预处理；对去噪信号进行CEEMD分解得到一系列本征模态函数(IMF)；然后计算各个IMF和去噪信号的互相关系数，通过设定互相关系数阈值筛选有用IMF；最后将有用IMF重构并利用包络谱对重构信号提取故障特征频率。实测信号表明：所提出的方法能降低噪声干扰并有效提取故障特征信息，证明该方法在噪声环境下具有较高的可行性和较强的实用性。

Bearing Fault Feature Extraction Method Based on WTD and CEEMD

Aiming at the problem that bearing fault signals are often mixed with noise and the fault features are difficult to accurately extract,a bearing fault feature extraction method was proposed based on wavelet threshold denoising (WTD) and complementary ensemble empirical mode decomposition (CEEMD).The WTD was used to denoise the original signal. The denoised signal was performed CEEMD decomposition to obtain a series of intrinsic mode functions (IMF);then the correlation between each IMF and the denoised signal was calculated.The useful IMFs were obtained by setting the threshold of the cross-correlation coefficient.Finally,the useful IMFs were reconstructed and the envelope spectrum was used to extract the fault characteristic frequency of the reconstructed signal.The measured signals show that the proposed method can reduce the noise interference and effectively extract fault characteristic information,which proves that it has high feasibility and strong practicability in a noisy environment.