# Clutch Pressure Plate Temperature Prediction Based on Bi-LSTM and Migration Learning
## 4 Conclusions  
During the operation of the clutch, especially in the process of continuous engagement and separation, the accurate temperature prediction of the pressure plate is very important to maintain the performance of the clutch. Although some temperature prediction methods have been used, the accuracy and high computational difficulty of these methods is still a concern, and these methods are difficult to adapt to complex situations under real working conditions, such as with a small number of samples. Aiming at the pressure plate temperature prediction problem during the continuous clutch engagement and separation process, this paper proposes a new method based on Bi-LSTM and migration learning. First, neural network modeling is performed on the existing pressure plate temperature data of 930 s (31 consecutive engagements), and the features of pressure plate temperature are extracted using the Bi-LSTM neural network to establish a clutch pressure plate temperature prediction model; following this, the ADDA algorithm based on feature migration learning is used to realize the migration of the temperature prediction model to the temperature \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Position Indicator** & **Indicators** & **840-870 s** & **870–900 s** & **900–930 s** \\ \hline \multirow{8}{*}{a} & _MSE_ & 1.0107 & 3.0062 & 5.7527 \\  & _RMSE_ & 1.0053 & 1.7338 & 2.3985 \\  & _MAE_ & 0.7756 & 1.2945 & 1.7943 \\  & \(R^{2}\) & 0.9875 & 0.9553 & 0.9332 \\  & Maximum error & 3.4937 & 5.2216 & 5.9712 \\  & Maximum relative error & 1.12\% & 1.60\% & 1.82\% \\ \hline \multirow{8}{*}{b} & _MSE_ & 1.4780 & 4.8806 & 4.3443 \\  & _RMSE_ & 1.2157 & 2.2092 & 2.0843 \\  & _MAE_ & 0.9086 & 1.7853 & 1.5243 \\ \cline{1-1}  & _R_\({}^{2}\) & 0.9874 & 0.9569 & 0.9633 \\ \cline{1-1}  & Maximum error & 4.37 & 6.9208 & 6.5432 \\ \cline{1-1}  & Maximum relative error & 1.33\% & 2.08\% & 1.93\% \\ \hline \multirow{8}{*}{c} & _MSE_ & 3.0334 & 4.6094 & 4.6298 \\  & _RMSE_ & 1.7417 & 2.1469 & 2.1517 \\ \cline{1-1}  & _MAE_ & 1.6117 & 1.7687 & 1.8861 \\ \cline{1-1}  & _R_\({}^{2}\) & 0.9832 & 0.9691 & 0.9689 \\ \cline{1-1}  & Maximum error & 3.2611 & 4.8756 & 3.9925 \\ \cline{1-1}  & Maximum relative error & 1.15\% & 1.00\% & 1.40\% \\ \hline \hline \end{tabular}
\end{table} 
Table 8: The prediction indexes of Valeo clutch pressure plate. prediction model for the same type of pressure plate and the different type of pressure plate and to obtain the temperature prediction migration model. Then, the migration learning was utilized to predict the temperature profile for the next three consecutive engagements and compared with the experimentally collected data. The comparison results show that the model-predicted values and the actual values are in better agreement, the maximum temperature deviation is less than 9 \({}^{\circ}\)C, and the maximum relative deviation is less than 3.5%. In general, the proposed method can predict the temperature profile of the clutch pressure plate accurately in the case of small samples and limited data, which has important significance for the temperature control and the structural design of the clutch assembly and the similar fields. Conceptualization, D.C.; methodology, D.C.; software, D.C.; validation, D.C.; formal analysis, D.C. and Y.G. ; investigation, D.C.; resources, D.C., B.X. and Y.G. ; data curation, C.L. ; writing--original draft preparation, D.C.; writing--review and editing, Y.G. ; visualization, D.C.; supervision, D.C.; project administration, D.C.; funding acquisition, D.C. All authors have read and agreed to the published version of the manuscript. National Natural Science Foundation of China: No. 51965012; Innovation Project of Guangxi Graduate Education: 2023 YCXS006. Not applicable. Informed Consent Statement: Informed consent was obtained from all subjects involved in the study. Not applicable. The authors gratefully acknowledge the financial support for this work from the National Natural Science Foundation of China (NSFC) (No.