The Local Weighting Algorithm is Used to Model and Predict the Pressure Change of the Train Air Chamber

In recent years, with the continuous progress and development of society, the safety and efficiency of heavy-duty freight trains have become an important direction of railway development. With the rapid development of China's railway industry, railway transportation has the characteristics of heavy load, high speed and high density. Train air pressure information is of great significance to train safety braking. The purpose of this paper is to collect real-time data of a single train brake system in real time and predict train pressure information. The core of the local weighting algorithm is to assign a certain weight to each point near the predicted point, and then perform ordinary linear regression according to the minimum mean square error. This algorithm is very suitable for the model construction of this paper. In this paper, the local weighted regression model is used to establish the variation of pressure information with time. The accuracy is verified by continuously adjusting the model parameters based on the data, resulting in a better algorithm model. When the train brakes, the train tube first inflates the brake cylinder, and then the auxiliary cylinder inflates the brake cylinder. When the pressure reaches the set equilibrium, the deflation process stops. The air pressure of the brake cylinder is predicted based on the pressure of the train line and the auxiliary cylinder. Verify the predicted results and calculate the predicted loss values. The results show that the error is within an acceptable range. The experimental results show that the algorithm can better predict the cabin pressure.