# 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 Wheels  
The wheels of high-speed trains are important components for carrying, guiding, traction, and braking. When the train is in normal service, due to the long-term interaction between the wheels and the track, it will have different degrees of wear and tear and even cracks, which affects the stability of the train. Therefore, in order to improve the safety and reliability of train operation, early failure of wheels (wear, cracks, etc.) should be detected as soon as possible, and maintenance should be carried out as soon as possible. Their main diagnostic techniques are shown in Figure 6.  
To accurately identify defects in the wheels of high-speed trains, [PERSON] et al. [63] proposed a method based on fuzzy logic, which was experimentally shown to reduce the maintenance cost of trains. To be able to detect wheel defects in real time, [PERSON] et al. [64] proposed a real-time detection method based on a Bayesian dynamic linear model, which can detect the degree of wheel damage and show the abnormal values. To obtain the effect of seasonal changes on wheels, [PERSON] et al. [65] presented a method based on three Bayesian data-driven models with field monitoring data showing that the wear is more severe during the summer season than in other seasons. Due to the great difficulty of wheel fault detection in high-speed trains, [PERSON] et al. [66] advanced an algorithm of adaptive multiscale morphological filtering (AMMF) that can extract the wheel fault features in strong background noise. By establishing a wear model of the wheel, [PERSON] et al. [67] developed a physical data-drivenapproach to achieve accurate prediction of wheel performance degradation and effective assessment of the remaining service life. [PERSON] et al. [68] designed a dynamic differential evolution algorithm to identify defects and compared it with the chaotic particle swarm algorithm and the genetic algorithm, and experimental results showed that the method is more effective. [PERSON] et al. [69] introduced an adaptive noise canceling (ANC) method, and the experimental results showed that the method could effectively reduce noise interference and extract fault features from strong background noise. To accurately predict the early defects of wheels and reduce the maintenance time of railroad vehicles, [PERSON] et al. [70] proposed a method to predict wheel wear. [PERSON] et al. [71] collected signals and assessed wheel wear by installing various sensors (vibration acceleration, displacement, etc.) in the carriage, bogie, and wheelset. To identify fatigue cracks in high-speed train wheels more effectively, [PERSON] et al. [72] developed a method based on the energy entropy of EMD and the Elman neural network, which uses the energy entropy of EMD as a feature and the Elman as a classifier.  
Figure 5: Fault diagnosis method of bogie suspension system: **(a)** multi-domain fusion (MDF)–convolutional neural network (CNN); **(b)** linear local tangent space alignment (LLTSA) [58]; **(c)** functional model (FM) [62].  
Therefore, the fault diagnosis of train wheels is mainly achieved by feature extraction and pattern recognition. In feature extraction, morphological filters, ANC, AMMF, EMD, and other methods are mainly applied. In pattern recognition, the main methods used include Bayesian and Elman neural networks, etc.