# 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 the Train Information Control System  
The information control system of high-speed trains mainly consists of a detection unit, a control unit, an input unit, an output unit, a communication unit, display equipment, and other auxiliary equipment. During the long service life of a train, the components of the information control system develop early failures, which affect the stability of the train. Therefore, it is important to carry out the fault diagnosis of the information control system of high-speed trains to ensure the safe operation of trains, and the main diagnostic methods applied are displayed in Figure 10.  
To quickly detect faults in the information control system of trains, [PERSON] et al. [94] introduced a big data-driven approach based on Hadoop, MySQL, and HDFS as the basic framework for realistic user-oriented fault diagnosis and fault prediction. To enable more accurate fault detection and isolation, [PERSON] et al. [95] designed a method to improve the Petri net, which uses PCA to monitor the state of the vibration signal from the acquisition and can achieve the detection and isolation of information control system faults. To achieve a rapid diagnosis of early faults in information control systems, [PERSON] et al. [96] developed an approach based on a train safety sensor network (TSSN), which is accomplished in concert with a multi-layer sensor, a train data center, and a ground data center. [PERSON] et al. [97] advanced a method based on support vector regression (SVR) and Kalman filter (KF), which not only reduced the calculation time but also improved the accuracy of state prediction of the information control system. Aiming at the inaccuracy of fault diagnosis in the train information control system, [PERSON] et al. [98] presented a method based on a vehicle-mounted deep belief network (DBN), which was verified by actual fault data of the Wuhan-Guangzhou high-speed railway and was better than the traditional method. In order to diagnose the faults of the information control system of high-speed trains more comprehensively, [PERSON] et al. [99] proposed a method based on a deep fault tree  
Figure 9: Fault diagnosis method of electrical system: (**a**) convolutional neural network (CNN) [90]; (**b**) comprehensive weighting algorithm (CWA) [91].  
and quantitative analysis, and the effectiveness of the proposed method was verified by experiments. [PERSON] et al. [100] proposed a method based on fault trees and fuzzy D-S evidence inference, which was shown to be effective for fault prediction and diagnosis through experiments. To improve the accuracy of fault diagnosis in information control systems, [PERSON] et al. [101] designed a method based on a transferable belief model and multi-sensor data fusion, which was validated experimentally.  
Therefore, the fault diagnosis of an information control system is mainly made by pattern recognition, and the main methods used include PCA, the deep belief network, TSSN, the Kalman filter, the fault tree, D-S evidence, etc.