Novel models for fatigue life prediction under wideband random loads based on machine learning
This work addresses fatigue prediction for engineering applications, but it is incremental as it applies existing ML methods to a known problem.
The paper tackled fatigue life prediction under wideband random loads by developing three machine learning models (SVM, GPR, ANN), which outperformed traditional frequency-domain models in accuracy, with ANN showing the best performance.
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, i.e. support vector machine (SVM), Gaussian process regression (GPR) and artificial neural network (ANN). The generalization ability of the models is enhanced by employing numerous power spectra 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.