An intelligent fault diagnosis method based on adaptive maximal margin tensor machine

A Novel Method of Rolling Bearing Fault Diagnosis Under Strong Noise Backgrounds

Cyclic Symplectic Ramanujan Component Pursuit: Algorithm and Applications

Singular value penalization based adversarial domain adaptation for fault diagnosis of rolling bearings

Imbalanced Fault Diagnosis of Rolling Bearing Using Enhanced Generative Adversarial Networks

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

Abstract Inspired by the empirical wavelet transform (EWT) method, a new method for nonstationary signal analysis termed order-statistic filtering Fourier decomposition (OSFFD) is proposed in this paper. The OSFFD method uses order-statistic filtering and smoothing to preprocesses the Fourier spectrum of original signal, which improves the problem of sometimes unreasonable boundaries obtained by EWT directly 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 (FDM). The OSFFD method also is compared with the existing nonstationary signal decomposition methods including empirical mode decomposition（EMD）, EWT, FDM and variational mode decomposition（VMD） through analyzing simulation signals and the result indicates that OSFFD is less affected by noise and is much more accurate and reasonable in obtaining mono-components. After that, the OSFFD method is compared with the mentioned methods in diagnostic accuracy through analyzing the tested faulty bearing vibration signals and the effectiveness and superiority of OSFFD to the comparative methods in bearing fault identification are verified.