Work

Study on life prediction method for rail vehicle critical components based on deep learning models and track load spectra

Year: 2025

Type: article

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 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.

Source: High-speed Railway

DOI: 10.1016/j.hspr.2025.09.006

Authors: Haitao Hu, Quanwei Che, Weihua Wang, Xiaojun Wang, Ziming Wang

Language: English

Topic: Railway Engineering and Dynamics

Subfield: Mechanical Engineering

Field: Engineering

Domain: Physical Sciences

SDG: Industry, innovation and infrastructure

Open Access status: diamond

Funder: Coastal Response Research Center, University of New Hampshire

Award: G7424032148