A time-varying instantaneous frequency fault features extraction method of rolling bearing under variable speed

Under variable speed, the time-frequency analysis (TFA) method for processing rolling bearing vibration signal is susceptible to noise, which cannot extract accurate time-frequency features. Plus, the special meaning of each time-frequency feature remains unclear. Based on the dynamic characteristics of rolling bearing and combined with the improved multisynchrosqueezing transform (IMSST), empirical Fourier decomposition (EFD), and generalized demodulation (GD), a time-varying instantaneous frequency fault features extraction method of rolling bearing under variable speed is proposed in this paper. Firstly, the instantaneous frequency (IF) trajectory of the rotating shaft is accurately represented by the better time-frequency resolution of IMSST, and the demodulation phase function for GD is constructed by combining the fault characteristic coefficient (FCC) of the rolling bearing. Then, the components in the signal that match the demodulation phase function are mapped into constant IF components. Through adaptive EFD and inverse demodulation, the time-varying IF components satisfying the conditions are separated. Finally, based on the Hilbert transform (HT) and adaptive time-frequency spectrum (ATFS), a high-quality time-frequency representation (TFR) is reconstructed to extract the time-varying IF fault features and identify the defect type of rolling bearing. The simulation and measured signal analysis results show that the proposed method can accurately extract fault features of rolling bearings with different defects under variable speed and identify fault types, thus maximally avoiding the influence of noise and interference components.