Estimating load spectra probability distributions of train bogie frames by the diffusion-based kernel density method. (March 2020)

• Record Type: Journal Article
• Authors: Ma, Shuang; Sun, Shouguang; Wang, Binjie; Wang, Ning
• Is Part Of: International journal of fatigue. Volume 132(2020)
• Publication Date: 2020-03
• Subjects: Load spectra -- High-speed EMU -- Probability distribution -- Diffusion-based kernel density estimation -- Extrapolated load spectra; Materials -- Fatigue -- Periodicals; Materials -- Fatigue; Periodicals; 620.1122
• DOI: 10.1016/j.ijfatigue.2019.105352
• Languages: English
• ISSNs: 0142-1123

Abstract

Highlights: DKDE is optimal for estimating train bogie frame load distributions. DKDE avoids boundary bias while maintaining the PDF as a genuine PDF. Proper extrapolated load spectra of train bogies are obtained with DKDE. Abstract: The load spectra of a train bogie frame are used for bogie frame design, simulated fatigue testing and reliability evaluation and fatigue life prediction, however, modelling the bogie load spectrum is one of the most challenging tasks in this field. In this paper, diffusion-based kernel density estimation (DKDE) is used to obtain the load spectra distributions of train bogie frames. A full-scale B-type subway motor bogie and a full-scale high-speed EMU trailer bogie are analysed. DKDE, Gaussian kernel density estimation (GKDE) and parametric estimation are used to fit the distributions of the loads. Goodness-of-fit tests suggest that the distributions obtained by DKDE are close to the actual distributions. Fatigue damage are calculated to evaluate the distributions estimated by DKDE and GKDE. From the distributions obtained by DKDE, reasonable maximum load values and reasonable extrapolated load spectra are deduced.