Sub\|rail Foundation Disease Identification Based on Adaptive Particle Swarm Optimization Algorithm

Based on vehicle\|track coupling dynamics model, the simulation analysis of the sleeper vibration response under different sub\|rail foundation disease conditions is carried out. It is proposed to adopt SVM (support vector machine) algorithm and PSO (particle swarm optimization) algorithm to identify the sub\|rail foundation basic diseases. To improve the convergence speed of PSO, an APSO (adaptive particle swarm optimization) algorithm is proposed, and the proposed method is applied to the identification and simulation of sub\|rail foundation basic diseases, so as to analyze the vibration characteristics of sleepers under different disease conditions. The research shows that the disease identification accuracy rate of the proposed algorithm can achieve over 80%, and the convergence speed of the algorithm is significantly improved.