A two-step data-driven method for predicting the wear of train wheel treads using GA-BPNN and LSTM algorithms

Abstract

ABSTRACT During train operations, the changing of wheel tread profile due to wheel-rail contact has a significant impact on the safe operation of the train and passenger comfort. Therefore, it is crucial to timely monitor the condition of the wheels and accurately predict wheel wear. In addition, wheel wear prediction can also guide wheel re-profiling, thereby extending the service life of wheels and minimizing operating costs. In this paper, a two-step data-driven wear prediction method was proposed to comprehensively predict wheel wear from two perspectives: the overall train and individual wheels. Firstly, Density-Based Spatial Clustering of Applications with Noise (DBSCAN) and Radial Basis Function neural network (RBFNN) were used to preprocess historical data, remove outliers, and construct time series data. By analysing the wear trend of wheels, a two-step data-driven prediction method was proposed. The first step is to predict the average Wear of wheels in the future time, and the second step is to predict the deviation between each wheel Wear value and the mean in the future time. Different prediction models were utilized and compared to obtain the best predictive performance. Results show that back propagation neural network (BPNN) optimized by genetic algorithm (GA) and long short-term memory (LSTM) based model yield better performance for predicting the wear trend of the overall train and individual wheels respectively. In addition, comprehensive contour parameters were utilized in the prediction model to enhance the accuracy and robustness.