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

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 (${S_e}$Se), 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.