When extracting the weak fault features of non-stationary vibration signals in a strong background noise environment, it was difficult to separate the system signals and the interference noises in the frequency band.The traditional denoising methods had great limitations.For this reason, we proposed a method based on the combination of the empirical wavelet transform (EWT) and the independent component analysis (ICA) to reduce noise.First, EWT was used to decompose the vibration signal, which avoids the modal aliasing and end-point effects of the empirical mode decomposition (EMD) and the ensemble empirical mode decomposition (EEMD).Then, according to the criterion of kurtosis and correlation coefficient, the corresponding IMFs were selected and the virtual noise channel was introduced.Finally, the reconstructed signal was demixed and denoised using the ICA, and the separated source signal was analyzed by the Hilbert envelope spectrum.The fault feature frequencies were extracted to realize fault diagnosis.Through the analysis of actual bearing signals, it was verified that this method has good filtering effect on time-varying and non-stationary strong background noise, and can extract the fault feature information more clearly and accurately.
Key words
rolling bearing /
fault diagnosis /
noise reduction /
empirical wavelet transform (EWT) /
independent component analysis (ICA)
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