Order-statistic filtering Fourier decomposition and its application to rolling bearing fault diagnosis

Abstract

Inspired by the empirical wavelet transform method, a newly nonstationary signal analysis method–termed order-statistic filtering Fourier decomposition is proposed in this article. First, order-statistic filtering Fourier decomposition uses order-statistic filtering and smoothing to preprocess the Fourier spectrum of original signal, which resolves the problem of unreasonable boundaries obtained by empirical wavelet transform in segmenting the Fourier spectrum. Then, the mono-components with physical significance are obtained by adaptively reconstructing the coefficient of fast Fourier transform in each interval, which improves the problem of too many false components obtained by Fourier decomposition method. The order-statistic filtering Fourier decomposition method is compared with the existing nonstationary signal decomposition methods including empirical mode decomposition, empirical wavelet transform, Fourier decomposition method, and variational mode decomposition through analyzing simulation signals, and the result indicates that order-statistic filtering Fourier decomposition is much more accurate and reasonable in obtaining mono-components. After that, the order-statistic filtering Fourier decomposition method is compared with the mentioned methods in diagnostic accuracy through analyzing the tested faulty bearing vibration signals and the effectiveness of order-statistic filtering Fourier decomposition to the comparative methods in bearing fault identification are verified.