# Clutch Pressure Plate Temperature Prediction Based on Bi-LSTM and Migration Learning
## 1 Introduction
Based on train braking process principles and timing data collected from an intelligent experimental platform, a baseline was established to solve the timing prediction problem with fixed grouping and multiple grouping. Additionally, a prediction algorithm for model validation and updating, oriented towards lifelong learning, was proposed. [PERSON] [12] employed a machine learning approach to learn optimal methods for engaging a transmission clutch, seeking fast and smooth engagement with minimal torque loss. An open-loop control signal was learned using an approximation model and then applied to an actual wet clutch to determine the optimal engagement method. [PERSON] [13] developed a systematic approach for detecting degradation in Thermoelectric Cooler Controller (TCC) systems using machine learning techniques  
Figure 1: Clutch pressure plate faulty parts. and domain expert knowledge. Validation using vehicle data showed the effectiveness of the method. Early detection of TCC degradation may help to extend the life of the TC, protect transmission components from further damage, and avoid limping and walking home incidents. [PERSON] [14] proposed a new Particle Swarm Optimization-Extreme Learning Machine (PSO-ELM) hybrid model for estimating day-by-day crop evapotranspiration in the Northwest Arid Zone with limited input data by optimally determining the parameters of the extreme learning machine model using a particle swarm optimization algorithm. [PERSON], [PERSON] [15] placed some neural networks first trained on an artificial but realistic dataset based on BIM simulations of real weather data. Then, an inference phase was performed on a second dataset collected with IoT devices. Finally, transfer learning techniques were utilized to improve prediction performance. [PERSON] [16] improved the initial parameter assignment method of KELM by using the GASA algorithm and established a GASA-KELM prediction model to predict the gas content. [PERSON] [17] proposed a method combining the asynchronous optimization of a two-dimensional deep convolutional generative adversarial network and a two-dimensional convolutional neural network for the intelligent diagnosis of advanced threaded connections. The general neural network needs a large number of samples. For the case of small sample provided, the transfer learning is brought out to construct the migrating neural networks where a pre-trained model is reused as the starting point for a model on a new task. By applying transfer learning to a new task, one can achieve significantly higher performance than training with only a small number of sample. In this paper, a method of clutch pressure plate temperature prediction based on a neural network model and migration learning is proposed. First, the Bi-LSTM neural network algorithm is used to train the clutch pressure plate temperature data to obtain a model. Although the model trained by the neural network algorithm performs better in fitting the training data, the model is only applicable to the data used in training and is unable to make future temperature predictions. Then, a transfer learning technique is introduced to train a new model on the new data based on the existing model to obtain a new model, which can integrate the features of the old and new data. To verify the feasibility and effectiveness of the method, the experiment of clutch engagement and disengagement under different operation conditions is performed, and the temperature profiles of the clutch pressure plate is measured by the thermal coupling installing on the pressure plate. The temperature data from the same model of the platen and temperature data from different models of the platen are analyzed, respectively. Three sets of experimental data throughout the entire process are used for training the neural network.