A hybrid particle swarm optimization algorithm (HPSO) to implement matching pursuit is developed, where BFGS (Broyden,Fletcher,Goldfarb and Shanno)method is combined with particle swarm optimization algorithm (PSO) to speed up the local search, and mutation operation is embedded to avoid premature convergence. The HPSO can overcome the disadvantage of poor convergence rate and decomposition accuracy existing in traditional optimization algorithms. Compared with using the single PSO and genetic algorithm to implement matching pursuit in the impulse atoms dictionary, the identification accuracy and speed to signal characteristics are improved through computation simulation. Meanwhile, the periodic impulses are extracted in joint time-frequency domain, and the single point defect in inner race of the rolling element bearing is identified in the rotation machine test rig accordingly. Results show that the matching pursuit using HPSO is to some extent applicable and effective.
ZHANG Jian-jun;WANG Zhong-sheng.
MATCHING PURSUIT BASED ON HYBRID PARTICLE SWARM OPTIMIZATION ALGORITHM[J]. Journal of Vibration and Shock, 2010, 29(1): 143-147
PDF(1668 KB)
