Feature Extraction of Rolling Bearing Faults Based on VMD and FRFT

The fault signal of rolling bearing is non-stationary nonlinear signal, and it is difficult to extract the feature of weak fault under strong background noise. This paper uses a new filtering method-Fractional Fourier Transform (FRFT). Compared with the traditional Fourier transform (FFT), it can make the time-frequency characteristics of unstable fault signals better displayed and suppress cross-interference. In this paper, the method of feature extraction of rolling bearings combined with Variational Mode Decomposition (VMD) and Fractional Fourier Transform (FRFT) is used. First, the original vibration signal is decomposed by VMD to obtain several intrinsic mode component functions (IMF). The component with the largest correlation coefficient is selected as the optimal component for filtering in the fractional order domain. The 1.5-dimensional envelope spectrum of the filtered signal is analyzed , The frequency value corresponding to the maximum amplitude can be obtained. This frequency value is the fault characteristic frequency of the rolling bearing. The simulation results show that the method can effectively extract the fault characteristic information of the rolling bearing.