Abstract
This paper investigates the system reliability analysis under random and interval variables (SRA-RI). When performance functions in SRA-RI are replaced by surrogate models, it is found that the composite projection outlines on the composite limit-state surface should be well approximated so as to accurately assess system failure probability bounds. Then a composite-projection-outline-based active learning Kriging (CPOK) method is proposed in this paper. To refine the approximated composite projection outlines, three system learning functions are defined in CPOK for parallel, series and mixed systems, respectively. Based on these three functions, new points around the composite projection outlines are sequentially selected and used for the update of Kriging models. Meanwhile, prediction uncertainties of Kriging models are quantified and used for terminating the update of Kriging models. Finally, system failure probability bounds are evaluated by Monte Carlo simulation. The advantages of CPOK are validated by four numerical examples and a piezoelectric energy harvester example.
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