The feature extraction from noise contaminated vibration signals is very difficult.The synchrosqueezed wavelet transform(SST) was introduced in the process of vibration signal denoising.To solve the difficulty of slecting intrinsic mode functions (IMF)s, a novel denoising method for vibration signals based on the instantaneous frequency complexity and autocorrelation coefficient kurtosis threshold of IMF was presented. The SST signal analysis method was ultilized to extract IMF components and then the Hilbert transform was applied to obtain the instantaneous frequency curve.IMF components were selected for reconstruction according to their corresponding component instantaneous frequency complexity. In order to further eliminate the noise effect,the correlation coefficient threshold method based on kurtosis value was employed to select once more the components for reconstructiing the original signal which can finally realize the original signal noise reduction.By experiments,the feasibility and effectiveness of the proposed method were verified by using simulated noise signals with different standard variances and bearing vibration signals of logistics machinery transmission equipments. Compared with other methods based on the ensemble empirical mode decomposition and wavelet transform, the results demonstrate that the proposed method is better than others.
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