Prediction of high-speed train wheel-rail relationship based on maximum information coefficient and LSTM

Abstract

Aiming at the prediction of wheel-rail relationship of high-speed trains, a prediction method combining maximum information coefficient (MIC) and LSTM was proposed. Firstly, the dynamic model of high-speed train was established by SIMPACK and the original dataset was obtained. Secondly, the maximum information coefficient is used to preprocess the data. Then, a prediction model of wheel-rail relationship based on long short-term memory neural network was built. Adam optimizer was used to optimize the learning rate and network structure. Finally, the optimized long short-term memory neural network is used to predict the wheel-rail relationship. The prediction results of wheel-rail relationship show that when the number of model iterations is 45, and the number of hidden layers is 110, the average absolute error percentage of model prediction is the smallest, and the value is 0.0247. Under these conditions, the predicted result is very close to the real value, that is, the data pretreated by the maximum information coefficient can make the model accurately predict the change trend of wheel-rail relationship, which can provide support for further research.