Open in APP

System reliability analysis with small failure probability based on active learning Kriging model and multimodal adaptive importance sampling


System reliability analysis with small failure probability is investigated in this paper. Because multiple failure modes exist, the system performance function has multiple failure regions and multiple most probable points (MPPs). This paper reports an innovative method combining active learning Kriging (ALK) model with multimodal adaptive important sampling (MAIS). In each iteration of the proposed method, MPPs on a so-called surrogate limit state surface (LSS) of the system are explored, important samples are generated, optimal training points are chosen, the Kriging models are updated, and the surrogate LSS is refined. After several iterations, the surrogate LSS will converge to the true LSS. A recently proposed evolutionary multimodal optimization algorithm is adapted to obtain all the potential MPPs on the surrogate LSS, and a filtering technique is introduced to exclude improper solutions. In this way, the unbiasedness of our method is guaranteed. To avoid approximating the unimportant components, the training points are only chosen from the important samples located in the truncated candidate region (TCR). The proposed method is termed as ALK-MAIS-TCR. The accuracy and efficiency of ALK-MAIS-TCR are demonstrated by four complicated case studies.

Keywords: Active learning, Kriging model, Small failure probability, System reliability analysis

Author: Xufeng Yang, Xin Cheng, Tai Wang, Caiying Mi
Journal: Structural and Multidisciplinary Optimization( IF:3.9 )   Time:2020-02-14
DOI:10.1007/s00158-020-02515-5    [Quote]
Link:     Article     PDF

Comments    Read:5

Ftracker is a frontier tracker.
专业学术信息平台,免费并及时为您提供全球范围内各大期刊的最新学术信息。 “关键词内容订制”,”期刊浏览”,”数据库检索”,”期刊下载”,”评论讨论”。
Mobile App
IOS search "Ftracker" in AppStore
Ftracker学术犬: 970207982

文献求助群Ftracker: 698420805

新冠病毒交流Ftracker: 878107029

植物基因功能Ftracker: 556178713

©2020 Ftracker