The traction drive system of a high-speed train has a vital role in the safe and efficient operation of the train. This paper established an electromechanical coupling model of a high-speed train. The model considers the interaction of the gear pair, the equivalent connecting device of the transmission system, the equivalent circuit of the traction motor, and the direct torque control strategy. Moreover, the numerical simulation of the high-speed train model includes constant speed, traction, and braking conditions. The results indicate that the meshing frequency and the high harmonics rotation frequency constitute the stator current. Furthermore, both frequencies are evident during constant speed. However, they are blurry among other conditions except for twice the rotation frequency. Meanwhile, the rotor and stator currents’ root-mean-square (RMS) values during traction are less than the RMS value during braking. The initiation of traction and braking causes a significant increase in current. During the traction and braking process, the RMS value of the current gradually decreases. Therefore, it is necessary to pay attention to the impact of the transition process on system reliability.

Addressing the challenge of the current inability to qualitatively identify rail corrugation damage accurately from axle box acceleration data, this study proposes a novel approach. To indirectly identify rail corrugation from axle box acceleration, we introduce an improved successive variational mode decomposition (SVMD) algorithm, coupled with a deep learning model for corrugation recognition. First, a numerical model of the vehicle-rail-track slab system is established, considering rail corrugation. Indicator analysis is integrated into the SVMD. The improved SVMD is employed to decompose and reconstruct axle box acceleration, achieving noise reduction and extraction of useful components. Next, we apply the fast Continuous Wavelet Transform analysis method to effectively transform one-dimensional data into two-dimensional images. Finally, the You Only Look Once (YOLO) model serves as a classifier for the classification and recognition of corrugation with different wavelengths. The results demonstrate that the mechanism-driven improved SVMD effectively extracts corrugation components from axle box acceleration, while the YOLO model achieves rapid and efficient identification and classification of corrugation with different wavelengths. The results show that compared with other traditional models, the training time of the YOLO model is 60%–90% of the training time of the traditional algorithm, and the Recall rate is 1.15–1.45 times that of the traditional algorithm. In terms of wavelength identification, the YOLO model has a recognition rate of 98% for different wavelengths. The proposed approach offers an innovative and efficient solution for identifying rail corrugation damage in axle box acceleration data.

Cracks are one of the most common diseases of tunnel lining, and the structural dynamic response can be used to assess the health of a tunnel. Hence, this paper investigates the dynamic response of shield tunnel lining with a partly circumferential crack. The shield tunnel lining is regarded as a thin cylindrical shell and analyzed independently. The research methodology integrates the wave propagation method, the local flexibility matrix, the line spring model and the wave superposition principle. The results show that the position and depth of a partly circumferential crack can influence the natural frequency of the shield tunnel lining. Under the fixed-position load, as the distance from the monitoring point to the crack increases, the difference in displacement response amplitude between the undamaged and cracked linings diminishes. Moreover, deepening cracks enlarge the magnitude of amplitude differences. When the load approaches the crack, the radial amplitude difference first increases and then decreases as the monitor moves away from the crack. This finding helps determine the required monitor position. The displacement response of the selected monitor indicates that the closer the load position is to the crack, the larger the amplitude difference. The results aid in identifying the crack position and selecting corresponding load and monitor locations.