Prediction Method of Heavy Load Wheel/Rail Wear Mechanical Properties Based on GA-BP Hybrid Algorithm

Due to its large axle load and high-density operation mode, heavy haul transportation has greatly improved the cargo transportation capacity, and is receiving unprecedented attention from all countries in the world. Since the development of heavy haul freight transport in China, wheel rail wear has been paid much attention, especially the use of heavy axle load locomotives on upgraded heavy haul lines, which makes reducing wheel rail wear and damage become a technical problem to be solved urgently. Considering that there are too many mechanical parameters involved in the prediction of heavy load wheel rail wear mechanical properties, the prediction accuracy is reduced. Therefore, this paper proposes a method based on GA-BP hybrid algorithm to predict the mechanical properties of heavy load wheel/rail wear. Hertz contact theory is used to simplify the wheel rail contact relationship, and the wheel rail contact model is established. According to the wheel/rail contact model, the expressions of heavy load wheel/rail in the case of vertical, horizontal, direction and gauge irregularity are analyzed, and based on this, a mechanical model of heavy load wheel/rail wear is established. In order to solve the problems of slow convergence speed and easy to fall into local optimum of BP neural network in the prediction of heavy load wheel/rail wear mechanical properties, the global convergence of genetic algorithm is used to optimize the BP network. According to the obtained mechanical parameters of heavy load wheel/rail wear, the mechanical parameters are input into the optimized model, and the relevant prediction results are output. So far, the research on the prediction method of heavy load wheel/rail wear mechanical properties based on GA-BP hybrid algorithm has been realized. The experiment is designed from three aspects of wear degree, hardness and tensile strength, and compared with the measured value, reference [4] method, reference [5] method and reference [6] method to verify the effectiveness of the proposed method. The experimental results show that the predicted results of wear degree, hardness and tensile strength by this method are closer to the measured results. It is proved that the proposed method has higher prediction accuracy and better practical application effect.