# 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 Bearing  
Bearings are widely used in mechanical systems and are essential parts of high-speed trains. The bearings of trains work under heavy loads and alternating impact stresses for a long time and are highly susceptible to pitting, cracking, wear, and other failures. Their main diagnostic techniques are shown in Figure 2.  
To address the challenge of the large data volume of high-speed trains, [PERSON] et al. [7] proposed a method based on mapping approximate principal component analysis (PCA), and validated it with actual train operation data. To effectively analyze the vibration signals of bearings, [PERSON] et al. [8] presented a method based on empirical wavelet transform (EWT) to effectively detect outer ring faults, rolling body faults, and composite faults of bearings. Due to the relative motion between the train and the detection system, the collected acoustic signal is distorted by the Doppler effect, so [PERSON] et al. [9] introduced the method of multi-scale chirp path pursuit (MSCPP) and variable digital filter (VDF) to effectively suppress the Doppler effect and extract the bearing fault features. [PERSON] et al. [10] proposed a detec  
Figure 1: Fault diagnosis for key systems of high-speed trains. tion method based on shock response convolutional sparse coding, thus improving the accuracy of bearing fault detection. To extract bearing fault features effectively, [PERSON] et al. [11] proposed an acoustic detection technique based on a sparse decomposition of resonant signals and singular value decomposition, which is superior to traditional methods. To improve the diagnostic accuracy of train bearing faults, [PERSON] et al. [12] advanced a multiscale morphological filter method based on feature selection using maximum correlation based on more than 30 feature indicators of vibration signals. Through the technique of time domain interpolation resampling, [PERSON] et al. [13] proposed an enhanced spline kernelized wavelet transform-based acoustic detection method for roadside acoustics, which effectively overcomes the distortion caused by the Doppler effect. [PERSON] et al. [14] introduced a sparse representation method based on cyclic structure dictionary learning and applied it to the extraction of bearing fault features of trains, and the effectiveness of the proposed method was verified by simulated signals and measured signals. [PERSON] et al. [15] presented an improved harmonic product spectrum (IHPS) to identify multiple modulation sources hidden in the vibration signal, which not only effectively identifies the fault features but also eliminates the influence of non-fault modulation on the fault features. [PERSON] et al. [16] optimized the key parameters of variational mode decomposition (VMD) and proposed an improved scale-space VMD method, which can automatically decompose the resonance band of bearing signals and further reveal the fault mode. Through a time-frequency data fusion strategy, [PERSON] et al. [17] advanced a method of time-frequency distribution (TFD) that can effectively separate the acoustic signals in the time-frequency domain and achieve an accurate diagnosis of bearing faults. Minimum entropy deconvolution (MED) enhances the impulse component of the signal, and [PERSON] et al. [18] designed a particle swarm algorithm optimized MED method, and the experimental results showed that the method could effectively diagnose bearing faults with a low signal-to-noise ratio and outperforms other methods. Through the singular value decomposition package (SVDP) and reconstruction of the Hankel matrix, [PERSON] et al. [19] proposed a method of extended SVDP (ESVDP) and validated it with the failure data of train bearings. References [20,21] improved the conventional signal processing method and were able to successfully identify train-bearing faults.