The enhancement of fault detection for rolling bearing via optimized VMD and TQWT based sparse code shrinkage

Abstract

A typical vibration signal of fault bearing is composed of periodic repetitive transient impulses, multiple vibration disturbance and background noise. Variational mode decomposition (VMD) represents a potential tool for analyzing such signals. However, the reasonable selection of VMD algorithm parameters hinders its application in mechanical signal processing to a certain extent. According to the specific characteristics of rolling bearing fault signal, the composite dimensionless index is constructed as the objective function to ensure the optimal decomposition of VMD. To further enhance the fault characteristics, the tunable Q-factor wavelet transform (TQWT) along with sparse code shrinkage is proposed to denoise the modal components containing periodic impulses, which further highlights the impulses and improves the sparseness of fault signal. Simulation and experimental signal analysis verify the effectiveness and reliability of this method. The results show that the use of optimized VMD and TQWT based sparse code shrinkage dramatically sharpens the impulses from the mixed signal with noise interference and increases the sparseness to a level.