# Clutch Pressure Plate Temperature Prediction Based on Bi-LSTM and Migration Learning
## 1 Introduction  
The clutch is an important component in the automotive transmission system [1], which enables connecting and disconnecting the driving and driven parts. During operation, engine torque is transmitted through the relative sliding of the pressure and friction plates. The friction-induced heat increases the temperature of the pressure plate, which usually increases the thermal stress and deformation of the pressure plate, sometimes leading to clutch failure [2; 3]. As shown in Figure 1, the surface of the pressure plate shows severe ablation at high temperatures. Clutch failures due to high temperatures often exceed half of the total number. Consequently, the investigation of pressure plate temperature in clutches holds immense significance. [PERSON] [4] conducted a comprehensive investigation using a finite element method based on thermal buckling theory to analyze the thermal buckling characteristics of automotive dry clutch pressure plates under two typical thermal loads. The effects of temperature distribution along the pressure plate thickness direction and boundary constraints on the thermal buckling characteristics of the pressure plate were investigated. In a subsequent study, [PERSON] established a simplified model of the pressure plate assembly. This model simulated the deformation field of the plate under continuous sliding and natural coolingto room temperature. The mechanism leading to the undercutting deformation of the pressure plate was proposed based on equivalent von Mises stresses [5]. [PERSON] [6] addressed the problems of clutch pressure plate, which often suffers from thermal abrasion and thermal cracking, in the Solid Works software to establish a complete pressure plate solid model of the phi 280 clutch pressure plate. Then, a finite element model was established for analysis to calculate the thermal stress coupling to analyze the thermal and stress conditions of the pressure plate. [PERSON] [7] established a parametric two-dimensional finite element model and verified the validity of the model, which can be used for damage prevention and thermomechanical behavior analysis of clutches during transient operation. [PERSON], [PERSON] [8] established a friction clutch system (single plate clutch) mathematical model, solved the temperature field during the slipping phase (single plate engagement), and used an axisymmetric model of force generation to simulate the analysis of the friction clutch system during the engagement process. The experiment method of the clutch temperature faces the problems of high experimental cost, time-consuming data acquisition, and limited experimental conditions. Although the simulation calculation provides a cost-effective and controllable experimental environment, it still depends on the model's accuracy and the data input's reliability. [PERSON] [9] introduced an enhanced sparrow search algorithm to optimize the slope safety coefficient prediction model (ISSA-BP) in BP neural networks. The proposed model was improved by incorporating dynamic weighting factors and an inverse learning strategy, enabling adaptive search in both aspects. [PERSON] [10] attempted to use a deep learning technique (transfer learning) to diagnose various clutch faults, such as pressure plate breakage and pressure plate wear. Vibration signals were collected from a test rig operating at different clutch states under different load conditions and further processed and stored as vibration maps. Various hyper-parameters in migration learning such as train-test split ratio, learning rate, optimizer, and batch size were varied and the optimal hyper-parameters suitable for achieving high classification accuracy were found for each pre-trained network. [PERSON] [11] constructed a predictive analytics research framework for train braking systems, integrating machine learning, migration learning, and lifelong learning techniques.