Fault Diagnosis of Rolling Bearing Based on GA-VMD and Improved WOA-LSSVM - Junning Li; Wuge Chen; Ka Han; Qian Wang (IEEE Access, 2020)
To improve the fault identification accuracy of rolling bearings due to the problems of parameter optimization and low convergence accuracy, a novel fault diagnosis method for rolling bearings combining wavelet threshold de-noising, genetic algorithm optimization variational mode decomposition (GA-VMD) and the whale optimization algorithm based on the von Neumann topology optimization least squares support vector machine (VNWOA-LSSVM) is proposed in this manuscript. First, wavelet threshold de-noising is used to preprocess the vibration signal to reduce the noise and improve the signal-to-noise ratio (SNR). Second, a genetic algorithm (GA) is utilized to optimize the parameters of variational mode decomposition (VMD), and optimized VMD is adopted to extract the fault feature information. The VNWOA-LSSVM fault diagnosis model is built to train and identify the fault feature vectors. The proposed method is validated by experimental data. The results show that this method can not only effectively diagnose various damage positions and extents of rolling bearings but also has good identification accuracy.