@article{Hu2026, 
author = {Haitao Hu and Quanwei Che and Weihua Wang and Xiaojun Wang and Ziming Wang},
title = {Study on life prediction method for rail vehicle critical components based on deep learning models and track load spectra},
year = {2026},
journal = {High-speed Railway},
volume = {4},
number = {1},
pages = {10-20},
keywords = {Railway vehicle, Deep learning, Neural network, Life prediction, Vibration fatigue},
url = {https://www.sciopen.com/article/10.1016/j.hspr.2025.09.006},
doi = {10.1016/j.hspr.2025.09.006},
abstract = {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.}
}