Cepstrum-driven modulated empirical wavelet transform and its application in bearing fault diagnosis

Empirical wavelet transform (EWT) has a complete theoretical support and can adaptively separate modes with different characteristics from the frequency domain. Signal decomposition and mode extraction based on the empirical wavelet transform can obtain more accurate components. This paper proposes a modulated empirical wavelet transform driven by cepstrum under the basic framework of traditional EWT method. The most innovative point of this paper is to use the characteristics of cepstrum to update the waveform of trend spectrum and realize the function of separating different modes. The filtering process constructs filter banks covering the entire frequency band based on scaling functions and empirical wavelets. In order to enhance the fault characteristics from the filtering components, the amplitude of its spectrum was modulated based on the Fourier transform characteristics. Finally, the effectiveness of the algorithm is verified by using simulation signals and experimental signals provided by Case Western Reserve University.