This study aims to evaluate the fatigue failure probability of a bogie frame considering the variability of input parameters, including loading ( $L$ L ), endurance limit ( ${S_e}$ S e ), and fillet size ( $r$ r ), through a data-driven surrogate model. Mechanical tests were conducted to determine the mechanical properties of the material of the bogie frame while a combination of machine learning and FEA has been utilized to generate a dataset for the dynamic response of the bogie frame under main in-service fatigue loads. Nine machine learning-based surrogate models were constructed based on the actual response at a limited set of data points chosen by the Optimum space-filling algorithm, and their accuracy was investigated. It is found that the CatBoost model is the optimal algorithm to map the stochastic input parameters with the factor of safety as the output parameter and perform the reliability evaluation. Also, results reveal a fatigue reliability of 99.34% for the bogie frame under normal conditions, with a cumulative failure probability of less than 0.66% over a 30-year service life. Furthermore, the results show that the proposed machine learning-based approach is an efficient tool to evaluate the fatigue failure probability of the bogie frame with reasonable accuracy when a small set of training data is available. This study’s scope extends to providing comprehensive guidelines for employing machine learning methods for fatigue reliability analysis of complex vehicle structures in the presence of various stochastic variables.


    Access

    Check access

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    Probabilistic machine learning approach to reliability analysis of a bogie frame under dynamic loading


    Additional title:

    S. A. SANIKHANI ET AL.
    INTERNATIONAL JOURNAL OF RAIL TRANSPORTATION



    Published in:

    Publication date :

    2024-09-02


    Size :

    21 pages




    Type of media :

    Article (Journal)


    Type of material :

    Electronic Resource


    Language :

    English




    BOGIE FRAME AND BOGIE

    YU QINGSONG / YU ZHONGJIAN / LI LIHENG et al. | European Patent Office | 2024

    Free access

    Bogie frame and bogie

    TAN YUAN / WANG RUI / ZHOU MINJIE et al. | European Patent Office | 2021

    Free access

    Bogie frame and bogie

    AN BO / SUN LINXIANG / CAO DUO et al. | European Patent Office | 2023

    Free access

    Bogie frame and bogie

    YU QINGSONG / YU ZHONGJIAN / LI LIHENG et al. | European Patent Office | 2023

    Free access

    Reliability Analysis and Prediction of Metro Vehicles’ Bogie Frame

    Qin, Yong ;Zhang, Zhen Yu ;Shi, Jing Xuan | Trans Tech Publications | 2014