Optimization of impulsive noise filtering method for rolling bearing signal enhancement

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 used in the work. The α-stable filter is capable of attenuating the energy of impulsive components to a large extent, thereby establishing a prerequisite for subsequent fault feature extraction. However, the performance of the α-stable filter is greatly dependent on the appropriate selection of the parameter of the filter order. Thus, to avoid the blind selection of the filter order in the α-stable filter and further improve its performance, an optimized α-stable filter is proposed in this paper. Firstly, the classical particle swarm optimization (PSO) is used to combine with the α-stable filter for selecting an optimal parameter. Secondly, a new index is constructed by combining the Gini coefficient with envelope spectrum kurtosis (ESK). The two indicators are robust against outliers, making them suitable for impulsive environments. The optimization objective function of the PSO is enhanced using the new index. 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.