Fault diagnosis of rolling bearings based on variational mode decomposition and fractional synchroextracting transform

Abstract

In response to the issues of non-stationary and multi-component characteristics often presented by actual fault signals in fault diagnosis of rolling bearings, this article proposes a fault diagnosis method for rolling bearings based on Variational Mode Decomposition (VMD) and Fractional Synchroextracting Transform (FRSET). First, FRSET algorithm is proposed by replacing the Fourier Transform (FT) in the Synchroextracting Transform (SET) with the Fractional Fourier Transform (FRFT). By selecting an appropriate fractional variable, the frequency axis in the fractional domain can be rotated to an optimal position, resulting in improved time-frequency resolution and accuracy of the time-frequency spectrum for non-stationary signals. The simulation results demonstrate that the FRSET algorithm outperforms the SET algorithm in terms of time-frequency concentration and spectral accuracy. On this basis, a VMD-FRSET algorithm is further proposed by incorporating the VMD to resolve issues such as time-frequency domain ambiguity and low time-frequency resolution that arise when processing signal components with closely spaced frequencies in the FRSET method. The experimental results show that the VMD-FRSET algorithm achieves superior time-frequency concentration and precision compared with other methods in the article.