Based on the existing prediction models for wheel wear of the metro train,supported by a large amount of wheel profile data,this paper analyzes the law of wheel wear,such as the relationship between profile parameters and equivalent taper with tread wear and wheel flange thickness wear,and studies the correlation between each parameter and wheel wear.Using a data-driven approach,a Transformer based artificial neural network is constructed to estab-lish a wheel wear prediction model.The experimental results show that the data-driven wheel wear prediction network can achieve accurate prediction of wheel wear,which can be used to predict the service life of wheels,reduce related costs,and improve economic benefits.
Metro EMUWheel WearPrediction ModelTransformerArtificial Conicity
NETL
NSTL
万方数据
