## 2 Related Work  
High-speed trains are a type of equipment utilizing advanced technology and complex structures. Safety is the top priority in high-speed railway operation [1], which can be achieved through train fault diagnosis and prediction [9]. High-speed train fault diagnosis methods can be divided into manual diagnosis, automatic test equipment (ATE) diagnosis and built-in test equipment (BITE) diagnosis. Most existing mechanical fault diagnosis technologies need extensive professional knowledge to judge and select the appropriate features in the feature extraction process [10], which requires highly skilled technicians. Additionally, many unpredictable factors exist in the mechanical operation process, making fault diagnosis even more complex. Furthermore, current technology in manual intervention fault diagnosis has great limitations, which poses another problem [3]. The ATE and BITE fault diagnosis methods are both based on equipment status. With the development of artificial intelligence and deep learning [11], machine learning has become a popular method for learning effective internal features from train operation data for fault recognition, realizing the entire intelligence process from fault feature learning to classification, reducing human intervention as much as possible, and completing train fault diagnosis in an intelligent and effective manner. Much research has been done on high-speed train fault diagnosis methods. Fault diagnosis research based on evidence theory applies the improved DS(Dempster Shafer Theory) [12] method to trains for component fault feature extraction, which can effectively solve the fault diagnosis problems of multi-type sensors or data source fusion [13; 14]. Empirical mode decomposition (EMD) train fault diagnosis [15; 16; 17; 18] and feature dimension reduction [19; 20] can greatly improve the reliability of train equipment and reduce manual maintenance costs. However, due to the strong reliability of high-speed train systems, there is limited fault data on train equipment. With the development of artificial intelligence, numerous intelligent diagnosis methods have emerged, of which intelligent algorithms for small samples have been widely utilized in high-speed train fault diagnosis. HMM (Hidden Markov Model) has also been utilized [21; 22], in which a Markov chain is used to describe data changes and differences between Markov chains to achieve fault diagnosis. However, small data discrepancies cause the Markov chain to vary, so the generalization and robustness of an HMM-based train fault diagnosis method is very low. At present, a large number of scholars have introduced machine learning algorithms into high-speed train fault diagnosis. Fault diagnosis research based on support vector machines [4, 23, 24] uses SVM (Support Vector Machine) to train fault classifiers, which can effectively improve diagnosis accuracy. In neural-network-based fault diagnosis, the model is trained [5, 25] by using train operation historical data, and the trained model is applied to fault diagnosis of unknown samples, which can provide good diagnosis and prediction results. Every fault diagnosis method has its own advantages and limitations. In recent years, many researchers have proposed and developed multi-class fusion fault diagnosis algorithms, demonstrating that the technical advantages of these methods complement and combine with each other. Some studies [26, 27, 28, 29, 30, 31] also describe the fusion of different fault diagnosis technologies, which have achieved good results in certain application fields. At the same time, with regard to fault feature extraction, there have been many improved algorithms that eliminate the dependence on domain knowledge and expert experience [32, 33]; and as result, great breakthroughs have been made in the automatic extraction of mechanical fault features. However, due to increasingly prominent complex nonlinear and strong interference problems, there is no immutable general model for fault diagnosis.