Weighted kurtosis-based VMD and improved frequency-weighted energy operator low-speed bearing-fault diagnosis

Abstract

The diagnosis of low-speed bearing faults remains a challenging issue because background noise is often present and the impulse signal is prone to being masked. In this paper, we propose a low-speed bearing-fault diagnosis method using weighted-kurtosis variational-mode decomposition and an improved frequency-weighted energy operator (IFWEO). First, the raw signal is decomposed using VMD, and WK is employed to select the optimum intrinsic mode function to reconstruct the signal. The reconstructed signal carries abundant fault information. Second, a third-order cumulant method is introduced to improve the FWEO, and this method is able to strengthen the signal impulse and enhance the fault features. The IFWEO is able to effectively reduce the effects of noise. Third, the effectiveness of the proposed method is validated by simulation and engineering experiments, and the results show that the method presented here is able to accurately diagnose low-speed bearing faults.