A Time-Dependent Failure Possibility Analysis Method Based on Misclassification Probability Reduction and Contribution Learning Function

For addressing the time-dependent failure possibility (TDFP) under fuzzy uncertainty, the Kriging surrogate models combined with fuzzy simulation (FS) have achieved promising results. However, existing learning functions for Kriging do not comprehensively account for the predictive sign misclassification probability and the contribution of newly selected samples for the failure possibility. To further enhance the computational efficiency, this study proposes a new adaptive Kriging (AK)-based method for solving TDFP. This method first estimates the misclassification probability for critical performance signs via multivariate Gaussian distributions and then selects samples based on both their failure possibility contribution and misclassification probability. This strategy directs new samples toward critical failure regions with maximum joint membership functions (MFs), accelerating Kriging convergence. Three test examples and an engineering application of a simplified turbine blade validate the advantages of the proposed method, which can reduce the number of performance function calls while preserving accurate estimates of TDFP.