Novel models for fatigue life prediction under wideband random loads
  based on machine learning
Hong Sun Yuanying Qiu Jing Li Jin Bai Ming Peng
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.