The processing of rotor startup signals based on empirical mode decomposition

In this paper, we applied empirical mode decomposition method to analyse rotor startup signals, which are non-stationary and contain a lot of additional information other than that from its stationary running signals. The methodology developed in this paper decomposes the original startup signals into intrinsic oscillation modes or intrinsic modes function (IMFs). Then, we obtained rotating frequency components for Bode diagrams plot by corresponding IMFs, according to the characteristics of rotor system. The method can obtain precise critical speed without complex hardware support. The low-frequency components were extracted from these IMFs in vertical and horizontal directions. Utilising these components, we constructed a drift locus of rotor revolution centre, which provides some significant information to fault diagnosis of rotating machinery. Also, we proved that empirical mode decomposition method is more precise than Fourier filter for the extraction of low-frequency component.