# Prediction of Key Parameters of Wheelset Based on LSTM Neural Network
## 1 Introduction  
During the operation of a train, the wheelset plays a pivotal role in supporting the vehicle body, facilitating braking, and maintaining contact with the track. However, due to uneven distribution of the track curves, poor performance of the braking system, rail replacement, and lack of lubrication, the wheelsets may gradually experience abnormal conditions, such as wear on the tread surface, wear on the wheel flange, as well as scuffing and peeling of the tread surface [1]. The operational condition of the wheelset has a direct effect on the stability, safety, and comfortable operation of subway vehicles [2]. The wheel diameter, flange height, and flange thickness values, as key parameters of the wheelset, can reflect the potential risks in the operation of the wheelset [3]. Therefore, it is vital to predict the key parameters of the wheelset. With the development of rail transportation technology, an increasing number of methods have been applied to the prediction of wheelset parameters in rail vehicles. For example, [PERSON], [PERSON], and [PERSON] et al. implemented a new prediction model and applied particle swarm optimization algorithm on the basis of Kriging surrogate models, which is abbreviated as KSM-PSO, to achieve automatic adjustment of the scheme [4]. [PERSON], [PERSON], and others predicted the wheelset parameters by combining the models of artificial neural network (ANN) and recurrent neural network (RNN) [5]. Because these parameters aredifficult to obtain through experiments, the efficiency is very low if they are calculated by multi-body dynamics theory. Thus, improved real-coded genetic algorithm (IRGA), success history-based adaptive differential evolution (SHADE), bonobo optimizer (BO), and grey wolf optimizer (GWO), the four meta-heuristic techniques algorithms, have been adopted in the research to optimize the artificial neural network and recurrent neural network models. The results confirm that success history-based adaptive differential evolution (SHADE) is the best optimizer among the four meta-heuristic techniques adopted above, whose MAPE in tests is less than 7% for SHADE-ANN and SHADE-RNN. [PERSON], [PERSON], and others established a data-driven wheelset wear parameter prediction model based on LM-OMP-NARXNN [6]. LM-OMP-NARXNN is a nonlinear autoregressive model including exogenous input neural networks (NARXNNs), Levenberg Marquardt (LM), and orthogonal matching pursuit (OMP) algorithms. Their experimental results confirm that this method can obtain more effective and compact models with a smaller size. At the same time, compared with other prediction models and training algorithms used in NARXNN, this method has a higher accuracy in predicting the state parameters of the wheelset in the short term. [PERSON], [PERSON], and others built a dynamic model of high-speed railway vehicle with the SIMPACK software, and they used the Archard wear model based on this SIMPACK dynamic model to predict wheelest wear [7]. At the same time, taking the daily measurement data provided by Beijing Railway Administration as a reference, the wear was verified using BP neural network (BPNN) classification. The results of the combination of the two methods demonstrate that the wheel position in the bogie acquires great influence on the wheelset wear, while the position of the carriage in the train has barely no influence. The aforementioned research mainly analyzes the wheelset based on stable wear data. However, the abrasion prediction of the wheelset discussed in the above research is a unilateral prediction result, which only reflects the operating trend of the wheelset by predicting the wear of a single parameter. Firstly, because the wear change of the wheelset parameter is relatively small, the actual parameters of the wheelset cannot be accurately understood [8]. Secondly, the accuracy of the prediction result needs to be improved.