System reliability analysis based on dependent Kriging predictions and parallel learning strategy

Reliability analysis of a complex system is challenging because of complex failure regions and frequent requirement of time-consuming simulations. To address these problems, combining adaptive surrogate models with Monte Carlo simulation has received considerable attention in recent years. The core of existing adaptive methods is the construction of an effective learning function as the guideline to select new training samples. In this paper, a new learning function with a parallel processing strategy is proposed for selecting new training samples for complex systems. It combines dependent Kriging predictions and parallel learning strategy to further improve the computational efficiency. Using the proposed parallel learning strategy for system reliability problems, one or several new training samples can be selected at each iteration to refine the constructed surrogate models. This causes the total number of iterations to decrease. Compared with existing adaptive Kriging-based system methods, the proposed method offers the following advantages: (1) it is capable of parallel processing, i.e., multiple training samples can be selected at each iteration for refinement to reduce the overall computational time, (2) it is easy to implement for complex systems regardless of their structure, and (3) it is generally more effective than most existing methods. Three numerical examples are investigated to demonstrate the proposed method, and the results show that it has high applicability and accuracy for complex reliability problems.