# A Review of Fault Diagnosis Methods for Key Systems of the High-Speed Train
## 2 Fault Diagnosis of High-Speed Train Bogie System
### Fault Diagnosis of Train Suspension System  
The suspension system is an important part of the high-speed train, which mainly consists of the frame, primary suspension, secondary suspension, drive unit, etc. The suspension system plays an important role in power transmission and load bearing, and its performance directly affects the train's running quality and safety. Their main diagnostic techniques are shown in Figure 5.  
As the mileage of a high-speed train increases, its suspension system suffers from various types of fatigue damage and performance degradation, which increases the risk of train accidents. By taking advantage of the good extraction ability of deep learning, [PERSON] et al. [44] proposed a method based on a deep neural network (DNN) that can effectively identify multiple faults in a suspension system with an accuracy of 92.5%, which is better than traditional signal methods. To detect early faults in train suspension systems in a timely manner, [PERSON] et al. [45] calculated the correlation function of the vibration acceleration signals of a train under unbalanced forces and thus diagnosed the faults of the dampers. Due to the complex vibration signals and weak fault information of the train's suspension system, [PERSON] et al. [46] presented a method based on VMD multiscale entropy combined with SVM and validated it with experiments. Based on the analysis of the vibration signal features of different components of the suspension system, [PERSON] et al. [47] introduced a method based on the information entropy of the ensemble empirical modal decomposition to diagnose multiple fault types of the train suspension system effectively. [PERSON] et al. [48] proposed a fault identification technique based on wavelet entropy features for typical faults in train suspension systems. Due to the  
Figure 4: Fault diagnosis method of cardan shaft: (**a**) dual-tree complex wavelet packet transformation (DTCWPT) [41]; (**b**) double decomposition and double reconstruction (DDDR) [42]. strong noise and difficult feature extraction of the train suspension system, [PERSON] et al. [49] proposed a method based on an ensemble deep belief network (EDBN) and combined it with SVM to achieve a diagnosis. In addition, the application of ensemble empirical mode decomposition (EEMD) entropy and wavelet packet energy with least squares support vector machines as classifiers can effectively identify faults in suspension systems [50; 51]. To further improve the fault diagnosis accuracy of train suspension systems under small sample conditions, references [52; 53] designed methods based on combined permutation entropy and EEMD permutation entropy, respectively. By using the time-frequency domain features of vibration signals and convolutional neural networks (CNN), [PERSON] et al. [54] presented a multi-domain fusion (MDF)-based method to successfully diagnose faults such as air springs and transverse dampers of train suspension systems. [PERSON] et al. [55] advanced a feature extraction method based on energy entropy and singular entropy, and the effectiveness of the method was verified by simulated and measured signals of the train suspension system. Additionally, in order to improve the diagnostic accuracy of the suspension system, references [56; 57] developed the dual-tree complex wavelet and singular value decomposition, respectively. [PERSON] et al. [58] designed a method based on multiscale permutation entropy and linear local tangent space alignment (LLTSA), which was finally validated with tracking data of the suspension system of China's CRH3 high-speed trains. To detect early faults in high-speed train suspension systems as early as possible, [PERSON] et al. [59] introduced a method based on improved ToMFIR, which was then validated with data from the Chinese CRH2 and was better than the conventional method. To address the problem of great difficulty in early fault diagnosis of the anti-yaw dampers of the suspension system of high-speed trains, [PERSON] et al.