An efficient method for calculating system non-probabilistic reliability index

Abstract

Collecting enough samples is difficult in real applications. Several interval-based non-probabilistic reliability methods have been reported. The key of these methods is to estimate system non-probabilistic reliability index. In this paper, a new method is proposed to calculate system non-probabilistic reliability index. Kriging model is used to replace time-consuming simulations, and the efficient global optimization is used to determine the new training samples. A refinement learning function is proposed to determine the best component (or performance function) during the iterative process. The proposed refinement learning function has considered two important factors: (1) the contributions of components to system nonprobabilistic reliability index, and (2) the accuracy of the Kriging model at current iteration. Two stopping criteria are given to terminate the algorithm. The system non-probabilistic index is finally calculated based on the Kriging model and Monte Carlo simulation. Two numerical examples are given to show the applicability of the proposed method.

Reliability analysis of mechanisms with mixed uncertainties using polynomial chaos expansion

“…Therefore, the traditional single-source uncertainty model optimization design methods, especially those derived from probability theory, may no longer be applicable. [24][25][26] To solve the problem, nonprobabilistic methods [27][28][29][30] are explored.…”