Improved GA-PSO algorithm for feature extraction of rolling bearing vibration signal

Abstract To better extract the characteristics of rolling bearing vibration signals, the author proposes a method based on improved genetic algorithm-particle swarm optimization (GA-PSO) algorithm. The common time-domain and frequency-domain feature index construction vectors were extracted based on vibration signals, for signal prediction, by establishing an improved particle swarm algorithm, and by optimizing the signal feature model of the support vector machine (SVM), the signal of the rolling bearing was predicted. The experimental results show that: After the author’s improved particle swarm algorithm optimizes SVM, the signal characteristic accuracy of the bearing is significantly higher, the regression fitting curve is smoother, although the fitting trend is basically the same, the error is significantly higher, this shows that it is feasible to optimize SVM’s rolling bearing signal characteristics based on particle swarm optimization, and proved the author’s improvement of the particle swarm algorithm, it is effective in optimizing SVM parameters. It is proved that the improved GA-PSO algorithm can better extract the characteristics of the vibration signal of the rolling bearing.