Optimization of Classical Domains for High-Speed Train Transmission System

In order to monitor the operating condition of a high-speed train transmission system, matter elements that present the operating condition of components of the transmission system were established by the extension theory, and an optimization method for determining the classical domains of characteristic parameters of the matter element that presents a component's normal operating condition was proposed. In this method, a fitness function was constructed using the maximum comprehensive correlative degree (MCCD) between a component's sample sets and its normal operating condition. Then, the parallel particle swarm optimization (PSO) algorithm was adopted to solve the fitness function and determine the classical domains. In addition, the optimization results of classical domains were compared with those determined by the statistical method. The results show that the maximum and average of MCCDs obtained from the optimization results of classical domains are improved by 3.63% and 2.51%, respectively, which demonstrates that the optimization results of classical domains more conform to components' actual operating conditions.