# 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 Gear  
Gears are an important part of high-speed train transmission systems, which usually work under harsh environmental conditions (such as sudden shock loads, unbalanced shaft conditions, heavy loads, large speed fluctuations, etc.), resulting in gears inevitably cracking, pitting, wearing, spalling, suffering tooth surface damage, etc. Their main diagnostic techniques are shown in Figure 3.  
It is well known that gear failure is the main source of vibration and noise in high-speed trains. To effectively extract the fault features of train gears, [PERSON] et al. [33] designed an improved complementary ensemble empirical mode decomposition (CEEMD), which not only reduces the problem of modal aliasing but also reduces the residual noise in signal reconstruction and obtains valuable intrinsic mode functions (IMFs). In order to detect the early failure of cracks in gears, [PERSON] et al. [34] proposed a time-frequency statistical feature (TFSF) method based on finite element analysis combined with modal analysis, which not only obtained the causes of crack generation but also effectively identified the different degrees of crack failure. To address the problem of weak extraction of weak fault features under strong background noise, [PERSON] et al. [35] presented an improved maximum correlated kurtosis deconvolution (MCKD) method, which can effectively diagnose multiple faults in train gears and outperforms some conventional methods. To extract fault information from the vibration signals of train gears, [PERSON] et al. [36] advanced an improved empirical wavelet transform method based on EWT using the kurtosis index and inverse transformation technology and applied it to gear fault identification. Combining the advantages of spectral kurtosis and tunable Q-factor wavelet transform (TQWT), [PERSON] et al. [37] intro  
Figure 2: Fault diagnosis method of bearing: (**a**) multiscale chirp path pursuit (MSCPP) and variable digital filter (VDF) [9]; (**b**) time-frequency distribution (TFD) [17]; (**c**) generative adversarial network (GAN) [30].  
duced an improved TQWT, which was verified by simulation signals and experimental signals of train gears. [PERSON] et al. [38] proposed a method based on singular value decomposition (SVD) and multipoint optimal minimum entropy deconvolution adjusted (MOMEDA) and applied it to the fault diagnosis of train gears. In order to reduce the degree of modal aliasing in empirical mode decomposition (EMD), [PERSON] et al. [39] proposed a method based on a soft sieving criterion to optimize EMD and applied it to the simulated signals and the measured signals of train gear faults. [PERSON] et al. [40] developed a method to optimize VMD with a discrete difference evolutionary (DDE) algorithm, combined with MED for noise reduction, and successfully identified multiple faults in train gears.  
Therefore, gear fault diagnosis is mainly accomplished by fault feature extraction, and the main methods used include CEEMD, MCKD, wavelet transform, singular value decomposition, EMD, VMD, TFSF, etc.