## 1 Introduction  
As an important part of the traction transmission system, the gearbox affects the safety and stability of the Electric Multiple Unit (EMU). The components within the gearbox are subject to diverse loads, resulting in different types of malfunctions. Hence, analyzing operational data from the gearbox and anticipating potential failures are essential for ensuring the operational safety of the EMU. The traditional method of mechanical system fault diagnosis involves using fault samples with label information to construct a fault diagnosis model for rotating machinery gearboxes, decomposing the complex vibration signals into modal functions, and analyzing the single modes so as to reflect the local characteristics of non-smooth signals [1; 2; 3]. This method of modal decomposition is characterized by the instantaneous identification of features but requires a large amount of data to produce effective results [4; 5; 6]. With the development of artificial intelligence, deep learning methods have become the main research direction of current fault diagnosis due to the advantages of efficient and powerful feature extraction, and methods of deep learning mainly include support vector machines and neural network algorithms [7; 8; 9; 10]. The support vector machine still remains effective when the data dimension is larger than the number of samples, and it excels at solving nonlinear problems. However, SVM is sensitive to noisy data, not applicable to multi-class classification problems, and cannot directly provide probability estimates [11; 12; 13; 14]. Neural networks have self-learning functions, can extract useful information from data through learning, have the ability to find optimization solutions at high speed, and can quickly find solutions when dealing with complex problems. However, the training and development of neural networks usually require a large amount of computational resources, which may increase the cost [15; 16; 17; 18; 19; 20]. In rail train fault diagnosis research, due to the difficulty of obtaining fault characteristics from actual operation monitoring data, most scholars use accelerated life experiments or dynamics simulation to obtain the fault characteristics of key components and use deep learning algorithms to categorize and predict the fault characteristics [21; 22]. Generally, the frequency response function between acceleration signals is analyzed as a fault indicator by establishing a dynamic model of the train or a specific component and analyzing the impact of radial and axial vibration under different fault conditions [23; 24]. Then, using an RNN or CNN, normal and fault conditions are classified and recognized [25; 26; 27; 28]. However, in fault identification for EMU gearboxes, the complexity of the vibration response signal components makes it difficult to extract the characteristic signals. Additionally, the high cost of marking the fault samples through accelerated life experiments further complicates the process. How to carry out accurate fault prediction and diagnosis of key components of rolling stock through dynamics simulation and other cost-effective methods is the current problem of fault prediction that needs to be solved in the field of rail transportation. In summary, to solve the fault prediction problem of the scarcity of fault samples for the EMU gearbox, this paper establishes a rigid-flexible coupling dynamics model of the gearbox, taking into account the special characteristics of the CRH5 in the structure of the gearbox and the completeness of the collected data. The focus is on studying the universal joints, driving gear, and driven gears, which are the most frequent faults of the gearbox system of CRH5, as the objects of the study. Based on the rigid-flexible coupling dynamics model of the gearbox, the dynamics simulation of the whole life cycle degradation process of the key components is carried out.