Load spectrum and fatigue damage analysis of the bogie frame under multiple factors affecting service degradation of high-speed trains

Purpose The aim of this paper is to accurately evaluate the reliability of high-speed train bogie frames under degraded conditions. This study established a framework for compiling multi-factor-coupled degradation load spectra and performing fatigue damage analysis. Design/methodology/approach First, 12 potential degradation factors were identified based on a literature review, and 250 sample sets were generated via Latin hypercube sampling. By combining vehicle system dynamics modeling with multivariate linear regression analysis, seven key degradation factors were identified. Second, using an orthogonal experimental design, 49 degradation combination schemes were generated, and the full-life load spectra at critical connection points of the bogie frame were compiled for each scheme. Findings The analysis indicated that the degradation factors predominantly affected the high-amplitude range of the vertical load spectra at the secondary air spring (first-order amplitudes of 3.3–16.9 kN), whereas low- to medium-amplitude loads and their cycle counts remained relatively stable. Furthermore, a Gaussian process regression model was employed to quantify the influence of each degradation factor on load spectrum amplitudes, confirming wheel degradation as the primary contributing factor. Fatigue damage assessment revealed that under specific degradation combinations – particularly when the wheel polygon reached high orders (e.g. 25th order) – fatigue damage at the positioning arm supports and motor suspension mounts of the bogie frame could exceed 1.0, indicating a potential risk of fatigue failure. Originality/value This paper proposes a framework for developing load spectra for bogie frames that considers multiple factors of in-service degradation. It provides a reference for predicting the fatigue life and designing fatigue-resistant structures for high-speed train bogie frames under in-service degradation conditions.