Application of Human Cognitive Self Regulating Particle Swarm Optimization and Stochastic Resonance in Bearing Fault Diagnosis

Abstract

This paper presents a bearing fault diagnosis algorithm based on human self regulating particle swarm optimization and stochastic resonance. The algorithm first sets up the objective function according to the stochastic resonance structural parameters a and b. Then the self regulating particle swarm optimization algorithm is used to optimize the objective function, and the optimized optimal values a and b are substituted into the system of variable scale stochastic resonance. Finally, the measured bearing vibration signal to noise reduction and spectrum analysis to find the bearing fault characteristic frequency. Experimental results show that this method is effective in bearing fault diagnosis.