Wheel-Rail Force Continuous Measurement Based on Combinational Forecast Model

In order to accurately judge vehicle operation state, a wheel-rail force continuous measure method was put forward. Based on the wheel-rail interaction characteristics, the available data of wheel-rail force were extracted from test data by using the threshold value judgmental method. From the uncertainty and time variation of a wheel-rail force measurement system, a dynamic measurement sequence was regarded as a grey process, so the grey theory was used to continuously measure wheel-rail force. In order to improve prediction accuracy, the traditional GM(1,1) model was improved by combining the genetic algorithm and neural network. Ten prediction models were established to predict respectively, and then the predicted values with high accuracy were imported into series grey neural network to predict once again to improve the prediction accuracy and stability. The 10 prediction models were applied to wheel-rail force continuous measurement. The results show that the combination model based on the grey system, genetic algorithm and neural network has a high accuracy, and the average relative error is less than 2%. This combination forecast model can meet the requirement of wheel-rail force continuous measurement and reduce the influence of sensor failures on measurement results.