Empirical wavelet transform is an adaptive signal decomposition method in wavelet framework, which can decompose nonlinear and non-stationary vibration signals of the rotating machinery.Aiming at the problem of too much spectrum division in the process of traditional empirical wavelet transform, a new method of spectrum division and combination based on mutual information value was proposed, which can effectively reduce the number of frequency bands.Then, the component with the maximum kurtosis value was selected for signal reconstruction, and the minimum entropy deconvolution was used to reduce the noise of the reconstructed signal.The signal envelope analysis after noise reduction can effectively diagnose the weak fault of rolling bearings.Simulation signals and experimental signals were used to verify the method in the paper, which lays a foundation for further engineering applications.
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