Fault Diagnosis of Rolling Bearing Based on Fractional Fourier Instantaneous Spectrum

Fractional Fourier transform (FRFT) can transform data into the space of the fractional order domain, where fractional order can be used to search for the maximum value of fault. Instantaneous spectrum estimation is an important method to analyze non-stationary signals. Through it, the transient characteristics of these signals can be obtained in both time and frequency domain. A new fault diagnosis method for rolling bearing is proposed by combining instantaneous spectrum estimation with FRFT. Firstly, the optimal order of fractional Fourier transform is determined using the principle of maximum kurtosis coefficient. Then the 2-D fractional domain power spectrum under the selected fractional order is obtained using the rotation property of fractional Fourier transform. Furthermore, the energy intensity of each frequency component in the fractional domain is achieved by integrating the time-frequency spectrum along the time axis, and is applied to the fault diagnosis. The simulated signal and some actual bearing fault data are processed to verify the effectiveness with Renyi entropy introduced as an evaluation parameter. Experimental results show that the new algorithm has higher time-frequency resolution. Especially, there is a good aggregation for weak fault signals. The proposed method can obtain more accurate characteristic frequency identification and provide a new alternative for fault diagnosis of rolling bearing.

Automated summary

By combining instantaneous spectrum estimation with FRFT and determining the optimal order based on the principle of maximum kurtosis coefficient, this method achieves more accurate characteristic frequency identification, providing a new approach for fault diagnosis of rolling bearing.