Novel models for fatigue life prediction under wideband random loads based on machine learning

Machine learning as a data-driven solution has been widely applied in the field of fatigue lifetime prediction. In this paper, three models for wideband fatigue life prediction are built based on three machine learning models, that is, support vector regression (SVR), Gaussian process regression (GPR), and artificial neural network (ANN). All the three prediction models use the parameter b of the well-known Tovo-Benasciutti (TB) model as their outputs to realize fatigue life prediction and their generalization abilities are enhanced by employing numerous power spectrum samples with different bandwidth parameters and a variety of material properties related to fatigue life. Sufficient Monte Carlo numerical simulations demonstrate that the newly developed machine learning models are superior to the traditional frequency-domain models in terms of life prediction accuracy and the ANN model has the best overall performance among the three developed machine learning models. Three frequency-domain models are devised based on machine learning. SVR, GPR, and ANN models are employed for random fatigue life predictions. Three models are suitable for predicting fatigue life under wideband random loads. Three models outperform existing frequency-domain models in prediction accuracy.