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An Efficient Method for Time-dependent Reliability Problems with High-dimensional Outputs Based on Adaptive Dimension Reduction Strategy and Surrogate Model by Yuxiang Ji; Hui Liu; Ning-Cong Xiao; Hongyou Zhan is a Engineering article available to read on EtoBox.

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Time-dependent reliability analysis with high dimensional outputs is a challenge because of ‘curse of dimensionality’ and accurate reliability estimation over entire time interval is computationally expensive. In this paper, an efficient time-dependent reliability analysis method is proposed for systems with high dimensional outputs based on adaptive dimension reduction strategy and Kriging. The adaptive Kriging model is constructed in low-dimensional space after performing principal component analysis (PCA) on original time-dependent output. A new learning function and corresponding stopping criterion are developed as the guideline for selecting training samples at each iteration. The proposed learning function focuses on prediction accuracy over the entire time interval and the stopping criterion is directly linked to failure probability. Subsequently, fewer number of function evaluations is required compared with existing competitive works. Moreover, the key advantage of the proposed method is that it is a single-loop process, i.e., the selection of training samples, dimension reduction and Kriging modeling have been combined simultaneously at each iteration with adaptive manner

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Author

Yuxiang Ji; Hui Liu; Ning-Cong Xiao; Hongyou Zhan

Publisher

Elsevier BV

Published

2023

Language

EN

Field

Engineering (Physical Sciences)