A New Kriging-Based Learning Function for Reliability Analysis and Its Application to Fatigue Crack Reliability

Abstract

In order to solve the problems in fatigue reliability analysis of mechanical components, a new learning function based on the Kriging model is proposed. In the process of fatigue reliability analysis, surrogate model is often used to fit the implicit performance function to avoid large numbers of calculations of fatigue crack samples. The existing learning functions ignore the information of probability density function (PDF). To overcome this defect and avoid unnecessary sampling in low PDF regions, a novel learning function takes into account the PDF and the local accuracy of Kriging model. The accuracy of the Kriging model is improved by adding samples step by step, and the new training samples are determined by the proposed learning function. The proposed method is verified by two examples from literatures. The results show that, compared with other surrogate models and learning functions, the proposed method has advantages in efficiency, convergence and accuracy. Finally, the proposed method is employed to calculate the fracture failure probability of cracked structures.

Keywords

• Structural reliability analysis
• kriging model
• probability density function
• learning function
• Monte Carlo
• fatigue crack reliability