## 2 Related Work
Although the traditional neural network is effective for fault diagnosis, certain unsolved problems remain, and the deep learning model needs to be continuously optimized and utilized in fault diagnosis research and applications [34, 35]. Based on the deep neural network model, this paper proposes an improved fault diagnosis training model for high-speed train monitoring data, to further improve the accuracy and speed of fault identification. The deep-learning-based fault diagnosis model requires a large number of labeled training data for full training before accurate fault identification can be completed [31, 36]. However, the collection of labeled samples and accurate training require a great deal of computing time and resources. Based on high-speed train monitoring data, similar deep learning tasks have been conducted mostly in the cloud, with abundant computing and storage resources. However, if we only train through the cloud, we must consider the adaptability of various personalized diagnosis cases and train different application scenarios to achieve the appropriate model parameters. This, in turn, will lead to a significant increase in training parameters and heavy training tasks, which conflicts with the response time requirement of fault diagnosis. With the rapid development of Internet of things technology, an increasing amount of data are generated at the edge of the network; however, it is more efficient to process only certain data there [37]. Similarly, if we only rely on personalized application data to train through the edge end, the process will be limited by the computing power and storage capacity of the edge end, resulting in increased training time. At the same time, it is difficult to collect sufficient data to meet the needs of full model training. In mechanical fault diagnosis, experimental methods with excellent performance are often not suitable for practical applications. However, this kind of cross domain learning and knowledge transfer is still an important aspect of current research in this field [38, 39]. Using prior research on cross domain learning, this study incorporates the deep learning concept of combining cloud resources with high-performance computing and storage and edge devices with strong personalized adaptability and good time limit control [40]. In this paper, a real-time high-speed train fault diagnosis method based on edge and cloud collaboration is proposed, which aims to utilize the advantages of both cloud and edge computing, and conduct real-time fault diagnosis through the coordination of computing resources and real-time requirements. Fault data analysis is also a process of knowledge mining. Knowledge mining methods can be divided into four categories: linguistics, expert knowledge, subject and cognition [41]. When analyzing large-scale fault data, traditional diagnosis methods often face difficulties such as deep fault mechanisms, a large number of fault modes, and multi-fault information fusion. To solve these problems, it is necessary to establish fault diagnosis knowledge graphs based on information technology. Fault knowledge graphs [42] transform expert experience and diagnosis knowledge into machine-readable and permanently-stored data, which is crucial for fault data visualization. Based on high-speed train monitoring operation data, this paper presents a deep neural-network-based optimized fault diagnosis model and high-speed train health monitoring model based on edge cloud collaboration computing. In addition, advantages of the proposed model in terms of fault identification accuracy and timeliness are compared through experiments, to provide support for the fault visual analysis and health management of high-speed trains.