###### Abstract  
Safety is the most important aspect of railway transportation. To ensure the safety of high-speed trains, various train components are equipped with sensor devices for real-time monitoring. Sensor monitoring data can be used for fast intelligent diagnosis and accurate positioning of train faults. However, existing train fault diagnosis technology based on cloud computing has disadvantages of long processing times and high consumption of computing resources, which conflict with the real-time response requirements of fault diagnosis. Aiming at the problems of train fault diagnosis in the cloud environment, this paper proposes a train fault diagnosis model based on edge and cloud collaboration. The model first utilizes a SAES-DNN (stacked auto-encoders deep neural network) fault recognition method, which can integrate automatic feature extraction and type recognition and complete fault classification over deep hidden features in high-dimensional data, so as to quickly locate faults. Next, to adapt to the characteristics of edge computing, the model applies a SAES-DNN model trained in the cloud and deployed in the edge via the transfer learning strategy and carries out real-time fault diagnosis on the vehicle sensor monitoring data. Using a motor fault as an example, when compared with a similar intelligent learning model, the proposed intelligent fault diagnosis model can greatly improve diagnosis accuracy and significantly reduce training time. Through the transfer learning approach, adaptability of the fault diagnosis algorithm for personalized applications and real-time performance of the fault diagnosis is enhanced. This paper also proposes a visual analysis method of train fault data based on knowledge graphs, which can effectively analyze fault causes and fault correlation.  
edge intelligence; fault diagnosis; deep learning; edge cloud collaboration; visual analysis +
Footnote †: journal: Medicaldemand for computing resources. Therefore, it is necessary to optimize and learn the diagnosis model according to big data obtained from train monitoring. For trains with a single car body, it is crucial to deploy the fault diagnosis model on the edge, which will help predict and identify faults, diagnose current train operation faults in real time and provide fault location [6]. With the continuous development of Internet of things technology, edge computing, cloud computing and monitoring technology, we can combine these to develop a train running condition monitoring system based on edge cloud collaboration computing. This system will send on-site train running status data to the edge computing and cloud service center in real time for analysis. The data preprocessing will be done via edge computing, and in-depth analysis and additional tasks will be completed by the cloud service center [7]. Transfer learning will then be applied to distribute the trained model to the edge end, and the edge end will be used for fault identification, which will ensure the timeliness of fault diagnosis.  
At the same time, the offline monitoring data access and data analysis platform can be used for fault correlation analysis and fault category frequency analysis. Considering the visual view effect of fault analysis, the potential application value is mined from various data, which can build a more intelligent, efficient and accurate fault analysis system, and provide a basis for high-speed train health assessment.  
This paper mainly explores the intelligent fault diagnosis method and high-speed train applications based on intelligent edge computing [8]. Aiming at the problems of slow calculations, poor accuracy and weak anti-interference in fault diagnosis, this paper proposes a novel real-time fault diagnosis method for high-speed trains, which is suitable for the edge deployment of single car bodies and real-time diagnosis of current running fault conditions.