With the continuous expansion of road networks and the rise in railway transportation capacity, the scale of locomotive crew needs has increased sharply. However, the current training of locomotive crews is inefficient and costly and cannot meet the needs of the industry. This project adopted virtual reality technology to develop the driving and emergency skills of harmonious electric locomotive crews. The trainees can learn the composition and structure of the locomotive, master the working principles of the locomotive and receive training in relevant emergency skills in the virtual environment. After completion of the system development, we carried out a series of research studies and experiments; the results show that the use of this system to train personnel can lead to them quickly mastering the practical training content. This new training mode can effectively solve the outstanding problems with the current training system for locomotive crews.

The structural damage of rail vehicle bogie frames is mostly caused by fatigue cumulative damage under the action of multi-level complex loads. Under complex loads, the sequence effect and coupling effect between loads have a significant impact on structural fatigue damage. Meanwhile, the performance degradation of components during service also affects the damage accumulation and the remaining life of the structure. Traditional linear cumulative damage models and the Manson-Halford model ignore the influences of load sequence effect, load interaction, and structural degradation on fatigue life, leading to inaccurate remaining life predictions. By introducing degradation parameters to modify the Manson-Halford model, an improved nonlinear cumulative damage model is proposed, and its accuracy is verified by combining standard specimen fatigue test data. Finally, the improved cumulative damage model was applied to engineering practice in engineering to predict and evaluate the fatigue life of bogie frames.