# Slipping Trend Prediction Based on Improved Informer
## 5 Conclusions  
This paper proposes a novel slipping classification prediction method and describes its related adhesion characteristics, improved Informer algorithm, and self-attention mechanism. Based on the Informer regression prediction model, this method improves the input and output structure of the model, so as to classify and predict the slipping state. In this study, relative position embedding was used to process the input data to enhance the timing information. This method was proven to be effective through a large number of experiments. Compared with the traditional model, it has the following improvements:  
1. The proposed model can output several steps at the same time, and has a high accuracy and recall rate. 2. The detection speed is superior to that of other models, and it takes 3.11 ms for each forward propagation, meaning that the model can be used for online prediction. 3. This model can simultaneously predict the multi-axle slipping state of the locomotive, and the comprehensive index is good. The highest accuracy rate reaches 94.75%, the recall rate reaches 91.23%, and the F1 reaches 92.67%. 4. This paper considers some parameters which affect the slipping state and are difficult to analyze, such as the shaft speed, inverter voltage, and current. Therefore, compared with traditional methods, the method proposed in this paper is significantly improved in all aspects. Through many experiments on a large amount of data, it has been proven that the improved Informer model is effective in slipping detection. The method proposed in this paper was developed under ideal conditions. However, in actual operation, traction control is implemented through the motor, and current control methods output ideal motor torque commands. In practice, the motor torque output is subject to certain disturbances and fluctuations, which may affect the controller's performance. \begin{table}
\begin{tabular}{c c c} \hline
**Encoder** & & **Parameters** \\ \hline Inputs & 3 \(\times\) Convld & Embedding \\ \hline \multirow{4}{*}{ProbSpare self-attention block} & Multi-head ProbSparse attention & \\ \cline{2-3}  & Add, LayerNorm, dropout & \\ \cline{2-3}  & Pos-wise FFN, GELU & 11,776 \\ \cline{2-3}  & Add, LayerNorm, dropout & \\ \hline \multirow{4}{*}{Distilling} & 3 \(\times\) convld, ELU & \\ \cline{2-3}  & Max pooling & \\ \hline Decoder: & & Parameters \\ \hline Inputs & 3 \(\times\) Convld & Embedding & \\ \hline Masked & add mask on attention block & \\ \hline \multirow{4}{*}{Self-attention block} & Multi-head full attention & \\ \cline{2-3}  & Add, LayerNorm, dropout & \\ \cline{2-3}  & Pos-wise FFN, GELU & \\ \cline{2-3}  & Add, LayerNorm, dropout & \\ \hline Final & 2 \(\times\) Linear, Sigmoid & 596 \\ \hline \multicolumn{3}{c}{Total parameters:} & 20,788 \\ \hline Prediction speed: & 4.45 ms/step in CPU & 3.11 ms/step in GPU \\ \hline \multicolumn{3}{c}{Memory usage:} & 230 MB \\ \hline \end{tabular}
\end{table} 
Table 5: Details of the improved Informer network’s components and performance. Future research will further explore this issue by conducting joint experiments incorporating a motor model and a train model, based on the current framework. If possible, real-world vehicle tests will also be conducted. [PERSON] and [PERSON] designed the research, conceived the experiments, and drafted the manuscript; [PERSON] and [PERSON] collected the datasets and performed part of experiments; all of the authors reviewed the manuscript. All authors have read and agreed to the published version of the manuscript. This research was funded by National Natural Science Foundation of China--China State Railway Group Co., Ltd., Joint Fund for Railway Basic Research Project Funding U2468228. Not applicable. The original contributions presented in the study are included in the article, further inquiries can be directed to the corresponding author.