System reliability analysis through active learning Kriging model with truncated candidate region

Yuan, Xufeng; Liu, Yongshou; Cai-ying, MI; Tang, Chenghu

doi:10.1016/j.ress.2017.08.016

Cited by 103 publications

(25 citation statements)

References 30 publications

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“…Secondly, in ALK‐TCR, multiple Kriging models are built for all components while in ALK‐TCR‐HRA, only a single Kriging model is built for G ( x , y ). Thirdly, the TCR of HRA defined in this article is very different from that of SRA in Reference .…”

Section: Alk Model With Tcrmentioning

confidence: 98%

“…By such transformation, several brilliant ideas from the works of SRA can be borrowed into HRA. In SRA, several strategies have been proposed to approximate the components which really contribute to the failure of a system . In this article, the basic idea of ALK‐TCR is adopted, which avoids approximating the unimportant components by properly formulating a TCR .…”

Section: Alk Model With Tcrmentioning

confidence: 99%

“…As mentioned above, a Kriging model provides specific uncertainty information for G ( x , y ( j ) ), that is, G ( x , y ( j ) )∼ N ( μ G ( x , y ( j ) ), σ G ( x , y ( j ) )). Accordingly, the probability of G ( x , y ( j ) ) < 0 predicted by the Kriging model can be given as

P \{G (bold-italicx, y^{(j)}) < 0\} = Φ (- \frac{μ_{G} (bold-italicx, y^{(j)})}{σ_{G} (bold-italicx, y^{(j)})}) .

…”

Section: Alk Model With Tcrmentioning

confidence: 99%

“…Firstly, the HRA problem is originally transformed into a traditional system reliability analysis (SRA) problem without interval variables. By such a transformation, several brilliant ideas on building ALK models from the field of SRA can be potentially introduced into HRA . In SRA, there exists an unimportant region which does not influence the failure probability of system, and this region can be cut off from the candidate region .…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Bounds approximation of limit‐state surface based on active learning Kriging model with truncated candidate region for random‐interval hybrid reliability analysis

Yuan

Wang

et al. 2019

Numerical Meth Engineering

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Summary This article reports a brand‐new methodology based on active learning Kriging model for hybrid reliability analysis (HRA) with both random and interval variables. Unlike probabilistic reliability analysis, the limit state surface (LSS) of HRA is projected into a banded region in the domain of random variables. Only approximating the bounds of the banded region is able to meet the accuracy requirement of HRA. In the proposed methodology, the HRA problem is innovatively transformed into a traditional system reliability analysis (SRA) problem with numerous failure modes. And then a basic idea from the field of SRA is borrowed into HRA, and the so‐called truncated candidate region (TCR) for HRA is proposed. In each iteration, the negligible region which probably does not influence the bounds estimation of failure probability is truncated from the original candidate region, and the optimal training point is chosen from the TCR. After several iterations, the TCR will converge to the true ideal candidate region, that is, the candidate region without the inner part of LSS, and the added training points will be driven to the region around the bounds of LSS. The performance of the proposed method is compared with relevant methods by five case studies.

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Section: Alk Model With Tcrmentioning

confidence: 98%

Section: Alk Model With Tcrmentioning

confidence: 99%

P \{G (bold-italicx, y^{(j)}) < 0\} = Φ (- \frac{μ_{G} (bold-italicx, y^{(j)})}{σ_{G} (bold-italicx, y^{(j)})}) .

…”

Section: Alk Model With Tcrmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Bounds approximation of limit‐state surface based on active learning Kriging model with truncated candidate region for random‐interval hybrid reliability analysis

Yuan

Wang

et al. 2019

Numerical Meth Engineering

Self Cite

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“…On the other hand, network reliability metric calculates the probability that the system operates successfully under certain environmental conditions [5]. Failure probability of a system (in general) considering random variables at the input is the key principal of reliability analysis [6]. Many reliability evaluation measures for communication networks exist as State Space Enumeration method [7], Graph Reduction Technique [8], Tie-Sets [3], Cut-Sets [9], genetic algorithms [10], and Monte Carlo [11].…”

mentioning

confidence: 99%

Analysis, Modeling, and Design of a Reliable Wide Area Network Case Study for Tikrit University Intranet

Mahmood

Ali

Sileh

2018

Tikrit j. eng. sci.

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A B S T R A C TThis work presents the analysis and modeling of communication network used for data transmission with multi-protocols in campus network. The designed network is based on the geographical location of communication nodes. (Colleges and centers). Network optimal backbone is first designed by Kruskal algorithm. It will be subject to reliability improvement by links addition. Tie-sets method is used to evaluate the network reliability. Communication nodes are modeled using local area network (LAN), server, links, router, switch, and Firewall. Intranet will be used as communication backbone mainly to connect different communication nodes with the Principal Communication Center (PCC) where the System Server (SS) is located. The connection of Intranet to the Internet is mad via the front-end system server (SS). Tikrit University Intranet (TUI) is taking as case study in the present research. Tikrit University sites are grouped into master communication nodes. Each node is composed from several colleges, centers, and administrative sections.

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Adaptive Kriging‐based Methods for Failure Probability Evaluation: Focus on AK Methods

Mattrand

Beaurepaire

Gayton

2021

Mechanical Engineering Under Uncertainties

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System reliability analysis through active learning Kriging model with truncated candidate region

Cited by 103 publications

References 30 publications

Bounds approximation of limit‐state surface based on active learning Kriging model with truncated candidate region for random‐interval hybrid reliability analysis

Bounds approximation of limit‐state surface based on active learning Kriging model with truncated candidate region for random‐interval hybrid reliability analysis

Analysis, Modeling, and Design of a Reliable Wide Area Network Case Study for Tikrit University Intranet

Adaptive Kriging‐based Methods for Failure Probability Evaluation: Focus on AK Methods

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