Multivariate Enhanced Adaptive Empirical Fourier Decomposition and Its Application in Rolling Bearing Fault Diagnosis

Enhanced adaptive empirical Fourier decomposition (EAEFD) is a recently developed single-channel signal separation algorithm, which has attracted increasing attention for diagnosing localized rolling bearing failures. Even though the EAEFD approach can extract the fault characteristic information from the vibration signals, it has limited capability to comprehensively and accurately represent the bearing condition characteristic information. To tackle the drawbacks of EAEFD, in this article, the multivariate EAEFD (MEAEFD) approach is proposed to deal with the mode separation problem of multichannel signals for rolling bearings and realize the self-adaptive synchronous analysis of multivariate signals. To better consider the feature information of each channel, the MEAEFD-based mechanical fault diagnosis method is further proposed by fusing the multichannel feature information on the basis of the MEAEFD approach. The proposed MEAEFD approach is compared with multivariate empirical mode decomposition (MEMD) and multivariate variational mode decomposition (MVMD) methods by the simulated and measured signal analysis, which indicates that MEAEFD method has a certain superiority in terms of decomposition accuracy and robustness, and the proposed approach has better diagnostic accuracy than the compared approaches.