Research on Fault Diagnosis of Tuning Area of Jointless Track Circuit Based on PSO-SVM

Abstract

The ZPW-2000K jointless track circuit is composed of the main track and the small track in the tuning area. Aiming at the complexity and randomness of the tuning area failure, an intelligent diagnosis model of support vector machine(SVM) based on particle swarm optimization(PSO) is proposed. Firstly, according to the structural composition of the tuning area, 10 voltage and current monitoring quantities in ZPW-2000K track circuit monitoring system of passenger dedicated line are selected to form fault data feature set. Secondly, particle swarm optimization is used in MATLAB to optimize the SVM model parameters, the SVM diagnosis model with optimal parameters is obtained, then carry out fault pattern recognition. Through simulation calculation, the prediction results of PSO-SVM model are compared with traditional SVM model and genetic algorithm optimized SVM model, which proves that the algorithm is a new method to effectively evaluate the type of fault diagnosis, and realizes 7 types of fault identification in the tuning area. The classification accuracy of the model can reach 95% and has good fault diagnosis ability.