## 3 Methods
### Fault Identification System Model Based on Intelligent Edge Computing  
Currently, high-speed train control systems [43] collect operation parameter data from train components in real time. Then, the operation monitoring data that have accumulated in a fixed period of time are downloaded, stored and analyzed. Train fault diagnosis and analysis are extremely time-consuming processes, and completion time cannot be guaranteed. To solve the problem of real-time train operation fault diagnosis, we analyzed the advantages and disadvantages of cloud/edge end, and designed a train fault diagnosis model based on edge intelligent computing. The overall framework is shown in Figure 1. In the model, the cloud mainly stores the training sample data and trains the universal model. The training sample data is composed of personalized train diagnosis sample data sets from train operation condition monitoring. First, based on the abundance of training samples and computing resources in the cloud, the pervasive train operation fault diagnosis model is continuously trained and updated before finally obtaining the pervasive training results. The training results can then serve as different diagnosis scenarios, which can be used as intermediate results. The edge device collects the real-time train state data under the personalized working condition through the data sensing device, and then transmits it to the cloud for storage. At the same time, training samples of personalized working conditions are formed at the edge end to modify the cloud-to-edge end universal diagnosis model, so as to improve the applicability of specific diagnosis tasks and form a personalized model for real-time diagnosis of bearing faults. The data interaction between the cloud and edge in the framework consists of two main processes: (1) the edge will upload the locally stored personalized samples to the cloud at a specified frequency to enrich the training and verification sets of the cloud; (2) with the updating of data sets stored in the cloud, the universal diagnosis model parameters will be optimized through training, and the optimized model parameters will then be transferred to the edge to modify the model. The task of the edge end is to diagnose the train fault in real time and transfer the universal model parameters of the cloud to the edge end through transfer learning. In the edge controller, the local samples are used for personalized training of the diagnosis model,and the personalized diagnosis model is then generated, which can be applied to the edge. At this time, the real-time signal acquisition is loaded on the personalized diagnosis model, diagnosis results can be generated in real time, and the corresponding response can be made according to the diagnosis results. The fault diagnosis method in this paper can be generalized for the fault diagnosis of all key train components, which provides support for fault prediction of the entire train. In addition, the proposed fault visual analysis method can effectively analyze fault reasons and relationships between faults. At the beginning of the task, we used the edge data and open data set stored in the cloud for the universal training of the diagnosis model. During training, the samples were divided into training set and verification set according to proportion. After training a single SAE (stacked auto-encoder) model, the global SAES-DNN (stacked auto-encoders deep neural network) was trained greedily layer by layer, and parameters of the whole network were fine-tuned. The whole diagnosis process is shown in Figure 2. For the fault data identified in the cloud, fault analysis based on knowledge mapping was carried out to provide support for the fault cause and correlation analyses. Figure 1: Framework of the train fault diagnosis model, which consists of cloud and intelligent edge computing. The cloud trains the fault detection model and transplants the trained model to the edge through transfer learning.