AK-ARBIS: An improved AK-MCS based on the adaptive radial-based importance sampling for small failure probability

Yun, Wanying; Lü, Zhenzhou; Jiang, Xian; Zhang, Leigang; He, Peter

doi:10.1016/j.strusafe.2019.101891

Cited by 96 publications

(13 citation statements)

References 44 publications

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“…, G(x m )] is the corresponding response to X. The approximate relationship between any experiment x and the response G(x) can be denoted as [41,42]:…”

Section: Basic Theory About Kriging Methodsmentioning

confidence: 99%

“…For sampling tactics, importance sampling, subset simulation, line sampling, etc. have been adopted to evaluate failure probability in active Kriging (AK) reliability analysis, and some AK-based sampling methods were presented, such as AK-IS [37], AK-SS [38], AK-DS [39], AKOIS [40], AK-ARBIS [41], AK-SESC [42] and AKSE [43]. By combining these sampling methods with learning functions, the number of calls of limit state functions has reduced dramatically with acceptable accuracy of variation coefficient of failure probability.…”

Section: Akmentioning

confidence: 99%

See 1 more Smart Citation

AWK-TIS: An Improved AK-IS Based on Whale Optimization Algorithm and燭runcated Importance Sampling for Reliability Analysis

Qin¹,

Cao²,

Zhang³

2023

Computer Modeling in Engineering &Amp; Sciences

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In this work, an improved active kriging method based on the AK-IS and truncated importance sampling (TIS) method is proposed to efficiently evaluate structural reliability. The novel method called AWK-TIS is inspired by AK-IS and RBF-GA previously published in the literature. The innovation of the AWK-TIS is that TIS is adopted to lessen the sample pool size significantly, and the whale optimization algorithm (WOA) is employed to acquire the optimal Kriging model and the most probable point (MPP). To verify the performance of the AWK-TIS method for structural reliability, four numerical cases which are utilized as benchmarks in literature and one real engineering problem about a jet van manipulate mechanism are tested. The results indicate the accuracy and efficiency of the proposed method.

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“…, G(x m )] is the corresponding response to X. The approximate relationship between any experiment x and the response G(x) can be denoted as [41,42]:…”

Section: Basic Theory About Kriging Methodsmentioning

confidence: 99%

Section: Akmentioning

confidence: 99%

AWK-TIS: An Improved AK-IS Based on Whale Optimization Algorithm and燭runcated Importance Sampling for Reliability Analysis

Qin¹,

Cao²,

Zhang³

2023

Computer Modeling in Engineering &Amp; Sciences

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show abstract

“…In this case, the surrogate model methods are widely applied to improve the computational efficiency and perform more efficient reliability analysis of rotor system. Yun et al applied the active learning Kriging method with high-efficiency adding point technique to quantify the failure probability of Reliability analysis of aeroengine rotor system aeroengine turbine disk (Yun et al, 2020); Wang et al proposed a novel surrogate modeling approach by absorbing the strength of particle swarm optimization algorithm and leastsquares support vector regression to address the probabilistic flutter problems in aeroengine (Wang et al, 2020b).…”

Section: Surrogate Model Methodsmentioning

confidence: 99%

Recent advances in reliability analysis of aeroengine rotor system: a review

Song

Bai

2021

IJSI

107

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PurposeTo provide valuable information for scholars to grasp the current situations, hotspots and future development trends of reliability analysis area.Design/methodology/approachIn this paper, recent researches on efficient reliability analysis and applications in complex engineering structures like aeroengine rotor systems are reviewd.FindingsThe recent reliability analysis advances of engineering application in aeroengine rotor system are highlighted, it is worth pointing out that the surrogate model methods hold great efficiency and accuracy advantages in the complex reliability analysis of aeroengine rotor system, since its strong computing power can effectively reduce the analysis time consumption and accelerate the development procedures of aeroengine. Moreover, considering the multi-objective, multi-disciplinary, high-dimensionality and time-varying problems are the common problems in various complex engineering fields, the surrogate model methods and its developed methods also have broad application prospects in the future.Originality/valueFor the strong demand for efficient reliability design technique, this review paper may help to highlights the benefits of reliability analysis methods not only in academia but also in practical engineering application like aeroengine rotor system.

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“…The ARBIS method 33 is first developed to search the optimal sphere (reliability index) step by step in the standard normal space accompanying the adaptive selection of failure samples. To enhance the computational efficiency further, several researchers [34][35][36] have embedded the Kriging model into the ARBIS or similar sphere decomposition method. While in the framework of p-boxes, the bounds of failure probability need to be computed with a double-loop optimization model as aforementioned.…”

Section: Introductionmentioning

confidence: 99%

A novel method for reliability analysis with interval parameters based on active learning Kriging and adaptive radial‐based importance sampling

Wang

Zhou

et al. 2022

Numerical Meth Engineering

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For the reliability analysis of complex engineering structures, the estimation of the bounds of failure probability with interval distribution parameters is an important task when the perfect information of random variables is unavailable and the corresponding probability distributions are imprecise. The present work proposes an active learning Kriging‐based method combining with adaptive radial based importance sampling to compute the bounds of failure probability. For computing the bounds, the classical double‐loop optimization model is always investigated in the standard normal space. To decouple the computation, the inner‐loop optimization is addressed with the monotonicity of the commonly used probability distributions. When suffering the high dimensional problem, the dimension reduction method is introduced in monotonic analysis. While for the outer‐loop optimization, the normal space is decomposed with spheres, then the proposed method with an adaptive updated procedure is given. With this method, the bounds of failure probability can be estimated efficiently, especial for the rare event. Numerical examples are investigated to validate the rationality and superiority of the proposed method. Finally, the proposed method is applied to the reliability analysis of turbine blade and aeronautical hydraulic pipeline system with interval distribution parameters.

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AK-ARBIS: An improved AK-MCS based on the adaptive radial-based importance sampling for small failure probability

Cited by 96 publications

References 44 publications

AWK-TIS: An Improved AK-IS Based on Whale Optimization Algorithm and燭runcated Importance Sampling for Reliability Analysis

AWK-TIS: An Improved AK-IS Based on Whale Optimization Algorithm and燭runcated Importance Sampling for Reliability Analysis

Recent advances in reliability analysis of aeroengine rotor system: a review

A novel method for reliability analysis with interval parameters based on active learning Kriging and adaptive radial‐based importance sampling

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