A new learning strategy for analyzing multi-mode system reliability by considering the correlation effect of multiple Kriging models

Abstract

The Kriging model with numerical simulation can analyze reliability efficiently, but its extension in multi-mode system is troubled by the hard quantification of correlations among Kriging models of multiple limit state functions. For this issue, a new learning strategy (NLS) is proposed by considering correlation effect. Firstly, NLS accurately derives the cumulative distribution function (CDF) boundary of the system Kriging model, and it considers the correlations among Kriging models of all system modes. By this CDF boundary, NLS derives the upper bound probability of the system Kriging model misjudging candidate sample state, on which the most contributive sample is selected to improve the capability of system Kriging model judging system state. Secondly, NLS only adds most contributive sample to the training set of the most easily identified mode to avoid computational cost on updating the Kriging models of unimportant modes. Thirdly, by employing the upper bound of expected relative error of failure probability estimated by prediction and prediction mean of system Kriging model, a convergence criterion is used to improve efficiency under acceptable accuracy. The superiorities, including in selecting training point, updating mode and convergence criterion, of NLS over the up-to-date methods for analyzing the system reliability are demonstrated by examples.