Optimized impulsive noise filtering method for rolling bearing signal enhancement

Abstract

Abstract In this paper, we discuss the issue of bearing fault diagnosis in impulsive environments. Such impulsive signals have significant spike impulse characteristics and show the obvious non-Gaussian property. Compare to the cyclic impulsive signals generated by bearing local damage, the impulsive components can be considered to be a special kind of noise, namely impulsive noise. Unfortunately, the impulsive nature of the noise often leads to significant degradation of the performance of the signal processing techniques based on the Gaussian model. To overcome this issue, an impulsive noise filtering method based on Alpha-stable distribution (α-stable filter for short) is designed. Besides, to avoid the blind selection of the order parameter in the designed α-stable filter, the classical particle swarm optimization (PSO) is used to combine with the designed α-stable filter for an optimal parameter. We apply the optimized α-stable filter is applied to both simulated and real signals. The obtained results demonstrate that the filtering method is effective in canceling impulsive noise and enhances the ability to bearing fault detection.