# A Review of Fault Diagnosis Methods for Key Systems of the High-Speed Train
## 4 Fault Diagnosis of the High-Speed Train Electrical System and Information Control System  
### Fault Diagnosis of Train Electrical System  
High-speed train electrical systems are extremely complex and prone to performance degradation and early failure of their internal components due to various uncertainties (overload, unexpected disturbances, and extreme environmental changes). If these early faults are not detected in time, it affects the safe operation of trains. Therefore, it is important to perform the diagnosis of early faults in the train electrical system, and the main diagnostic methods applied are presented in Figure 9.  
The long-term operation of high-speed trains in various harsh environments makes it inevitable that the actuators and sensors in the electrical system will suffer performance degradation and even more serious failures. If not addressed in a timely manner, minor malfunctions can eventually turn into catastrophic consequences. To accurately diagnose early faults of asynchronous motors in high-speed trains, [PERSON] et al. [86] constructed a kinetic model based on a squirrel-cage induction motor with a d-q coordinate system, which has high sensitivity and accuracy. To address the problem of serious difficulty in detecting compound faults in electrical systems under multi-source disturbances, [PERSON] et al. [87] proposed a detection method that assigns different faults in coupled signals to separate subspaces, which can effectively improve diagnostic capability. To effectively detect early faults in the slanting support lines of the suspension chain network, [PERSON] et al. [88] presented a deep learning detection method based on fast RCNN Resnet 101 and YOLO V2, which achieves the detection of faults and their localization with high accuracy and robustness. [PERSON] et al. [89] proposed a deep learning foreign object detection method based on YOLO V3, which combines the advantages of migration learning and data augmentation and is not only easier to train but also improves the accuracy of foreign object detection in train electrical systems. To address the problems existing in the manual inspection of train electrical systems, [PERSON] et al. [90] advanced an automatic machine vision-based inspection method, which uses CNN to extract crack faults and then uses a fast algorithm to perform further identification of the region of interest. Combining the advantages of traditional hierarchical analysis, maximum absolute weighted residuals, and maximum entropy, [PERSON] et al. [91] developed a comprehensive weighting algorithm (CWA), and the experiments proved that the proposed method has good practical application value. To detect early faults of capacitors in train electrical systems, [PERSON] et al. [92] designed a method based on Dempster-Shafer classification fusion, which improves the accuracy of fault detection. To increase the accuracy of diagnosing different fault states in train electrical systems, [PERSON] et al. [93] proposed a discrete Hidden Markov method based on  
Figure 8: Fault diagnosis method of brake system: (**a**) refined inter-variate variance (R-IVV) [82]; (**b**) support vector machine (SVM) [83].  
k-mean clustering, which uses Lloyd's algorithm to quantify the collected sample vector set and can effectively identify six different kinds of faults.  
Therefore, the fault diagnosis of train electrical systems is mainly made by pattern recognition, mainly applying Resnet, CNN, CWA, YOLO, and discrete hidden Markov models.