Gaussian process regression based remaining fatigue life prediction for metallic materials under two-step loading

Remaining fatigue life prediction is vital for engineering structures to ensure safety and reliability. It can be more challenging when the structures suffer variable amplitude loadings because of the complex, non-uniform of the fatigue damage accumulation and inherent noise, uncertainty in the data. To further tackle the problem, the Gaussian process regression (GPR) is introduced, which can simultaneously estimate the output value and quantify the associated uncertainty. Therefore, a GPR-based remaining fatigue life prediction method is proposed to predict the remaining fatigue life for metallic materials under two-step loading in this paper. The proposed method is comprehensively evaluated on the dataset containing 12 materials, 328 samples in total. The proposed method achieves the lowest mean square error (MSE), mean absolute percentage error (MAPE), residual standard deviation (RSD) values and the highest correlation coefficient (CC) values among the six machine learning methods and the two model-driven methods. Those results indicate that the proposed method can achieve greater accuracy and reliability in remaining life prediction under two-step loading, which illustrate the effectiveness of the proposed method as a data-driven method in the field of remaining life prediction.

Automated summary

Remaining fatigue life prediction is crucial for ensuring the safety and reliability of engineering structures, particularly under variable amplitude loadings. This paper introduces a Gaussian process regression (GPR) method that can estimate the output value and quantify the associated uncertainty simultaneously. The proposed method achieves higher accuracy and reliability in remaining life prediction, as demonstrated by experimental results.