【摘要】 针对滚动轴承信号去噪及故障特征提取问题，提出一种基于SVD-CEEMDAN和KLD的滚动轴承故障诊断方法。该方法通过奇异值分解(SVD)对原始信号进行初步去噪，再利用完备集合经验模态分解(CEEMDAN)对去噪后的非平稳振动信号进行自适应分解，得到若干本征模态函数(IMF);然后通过KL散度法(KLD)筛选有效本征模态函数(IMF)重构，再对其进行自相关去噪；最后利用包络谱分析处理去噪信号，提取故障特征频率。通过对轴承实测信号进行分析，该方法可有效抑制噪声，并能清晰地得到反映实际故障信息的信号，证实所提出方法的实用性和有效性。更多还原

【Abstract】 In order to realize signal denoising and fault feature extraction of rolling bearings, a rolling bearing fault diagnosis method based on SVD-CEEMDAN and KLD was presented.Firstly, the original signal was denoised by SVD.Secondly, complete ensemble empirical mode decomposition with adaptive noise was used to adaptively decompose non-stationary vibration signal after denoising, and the intrinsic mode functions were obtained.Thirdly, the effective intrinsic mode functions were selected for reconstruction by Kullback-Leibler divergence, then autocorrelation noise reduction for the reconstructed signal was done.Finally, the characteristic frequency of fault was shown by envelope spectrum analysis of the denoising signal.The analyzed results of bearings validate that the proposed method can be used to effectively suppress noise and the signal reflected actual fault information can be gotten.The method is practical and effective.更多还原