TY  - JOUR
AU  - Hu, Haitao
AU  - Che, Quanwei
AU  - Wang, Weihua
AU  - Wang, Xiaojun
AU  - Wang, Ziming
PY  - 2026
TI  - Study on life prediction method for rail vehicle critical components based on deep learning models and track load spectra
JO  - High-speed Railway
SN  - 2097-3446
SP  - 10
EP  - 20
VL  - 4
IS  - 1
AB  - Deep learning and fatigue life prediction remain focal research areas in rail vehicle engineering. This study addresses the vibration fatigue of wheelset lifting lug in Chengdu Metro Line 1 bogies, aiming to develop a fatigue life prediction method for critical bogie components using deep learning models and measured track load spectra. Extensive field tests on Chengdu Metro Line 1 were conducted to acquire acceleration and stress response data of the wheelset lifting lug, generating training samples for the neural network system. Component stress responses were calculated via time-domain track acceleration and validated against in-situ stress measurements. Results show that neural network-fitted dynamic stress values exhibit excellent consistency with measured data, with errors constrained within 5 %. This study validates the proposed small-sample deep learning approach as an effective and accurate solution for fatigue life prediction of critical bogie components under operational load conditions.
UR  - https://doi.org/10.1016/j.hspr.2025.09.006
DO  - 10.1016/j.hspr.2025.09.006