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

• Published in: Reliability engineering & system safety Vol. 218; p. 108083
• Main Authors: Xiao, Ning-Cong, Yuan, Kai, Zhan, Hongyou
• Format: Journal Article
• Language: English
• Published: Barking Elsevier Ltd 01.02.2022; Elsevier BV
• Subjects: Adaptive Kriging; Adaptive systems; Complex systems; Computational efficiency; Computer applications; Computing time; Failure analysis; Iterative methods; Learning; Minimal path sets; Monte Carlo simulation; Numerical methods; Parallel learning; Parallel processing; Reliability analysis; Reliability engineering; Surrogate models; System effectiveness; System reliability; Training; Adaptive Kriging; Minimal path sets; Surrogate models; System reliability; Parallel learning; Complex systems
• ISSN: 0951-8320; 1879-0836
• DOI: 10.1016/j.ress.2021.108083

•A new reliability method is proposed based on dependent Kriging predictions and parallel learning strategy.•A parallel learning strategy is proposed for complex system reliability problems.•The proposed method is effective for complex system reliability problems.•The proposed method can significantly reduce overall computational time. 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.