A novel feature extraction method: iterative Ramanujan Fourier mixture spectrum

Abstract

As a classical demodulation method, envelope spectrum (ES) has been used in rotating machinery fault diagnosis. However, for strong noise or multiple faults, the feature extraction ability of ES is not outstanding, even if it is unable to extract obvious fault features. Fourier transform (FT) in ES analysis is also not suitable for analyzing non-stationary signals. Considering the shortcomings of FT, this paper defines the Ramanujan Fourier mixture transform (RFMT). The RFMT has outstanding period feature extraction ability and can accurately extract the periodic features in the envelope signal. Based on this, this paper proposes the iterative Ramanujan Fourier mixture spectrum (IRFMS) method. The IRFMS enhances the fault features in the signal by iterative envelope and selects the envelope signals with obvious fault features by cyclic kurtosis. The results of signal analysis verify that IRFMS has excellent fault feature extraction ability, which is suitable for various fields of rotating machinery fault diagnosis.