Small failure probability: principles, progress and perspectives

Lee, Ikjin; Lee, Ungki; Ramu, Palaniappan; Yadav, Deepanshu; Bayrak, Gamze; Acar, Erdem

doi:10.1007/s00158-022-03431-6

Cited by 9 publications

(3 citation statements)

References 269 publications

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“…29 In recent years, adaptive surrogate modeling methods have been rapidly developing, and the employed surrogate model is mainly the Kriging due to its unique variance prediction ability. [30][31][32] The core thought of adaptive Kriging (AK) is to adaptively improve the fitting precision of the LSF, by designing a learning function to acquire high-quality training samples. 33 Given the significant influence of learning function, a large number of Kriging-based learning functions have been presented, such as the least improvement function, 34 reliability-based expected improvement function, 35 weight learning function, 36 improved U function, 37 and hybrid learning function, 38 H-learning function.…”

Section: Introductionmentioning

confidence: 99%

“…In recent years, adaptive surrogate modeling methods have been rapidly developing, and the employed surrogate model is mainly the Kriging due to its unique variance prediction ability 30–32 . The core thought of adaptive Kriging (AK) is to adaptively improve the fitting precision of the LSF, by designing a learning function to acquire high‐quality training samples 33 .…”

Section: Introductionmentioning

confidence: 99%

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AOK‐ES: Adaptive optimized Kriging combining efficient sampling for structural reliability analysis

Huang,

Zhang,

Wang

et al. 2024

Quality & Reliability Eng

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The pivotal problem in reliability analysis is how to use as few actual assessments as possible to obtain an accurate failure probability. Although adaptive Kriging provides a viable method to address this problem, unsatisfied Kriging surrogate accuracy and reliability modeling samples often lead to an unacceptable computing burden. In this paper, an adaptive optimized Kriging combining efficient sampling (AOK‐ES) is proposed: first, to enhance the Kriging approximation ability, a high‐fidelity optimized Kriging model (OKM) is established; further, to ensure the samples quality of OKM modeling and reliability calculation, an improved Latin hypercube sampling method and an optimized importance sampling approach are developed correspondingly. Six different types of case studies demonstrate the superiority of the proposed AOK‐ES. The analysis results demonstrate that the proposed AOK‐ES holds the potential to reduce computing cost while ensuring accuracy.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

AOK‐ES: Adaptive optimized Kriging combining efficient sampling for structural reliability analysis

Huang,

Zhang,

Wang

et al. 2024

Quality & Reliability Eng

View full text Add to dashboard Cite

show abstract

“…By analyzing and processing these data, key features are extracted. The health status of the component is assessed from a number of perspectives such as the probability of failure [5][6][7][8][9], remaining life [10][11][12][13][14], and degree of condition deviation [15,16]. Park et al [17] obtained operational data of flywheel motors through ground-based acceleration experiments, anomaly detection, and fault prediction of satellite flywheel motors using shifted nuclear particle filters.…”

Section: Introductionmentioning

confidence: 99%

A Novel Approach to Satellite Component Health Assessment Based on the Wasserstein Distance and Spectral Clustering

Hui,

Cheng,

Jiang

et al. 2023

Applied Sciences

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This research presents a multiparameter approach to satellite component health assessment aimed at addressing the increasing demand for in-orbit satellite component health assessment. The method encompasses three key enhancements. Firstly, the utilization of the Wasserstein distance as an indicator simplifies the decision-making process for assessing the health of data distributions. This enhancement allows for a more robust handling of noisy sensor data, resulting in improved accuracy in health assessment. Secondly, the original limitation of assessing component health within the same parameter class is overcome by extending the evaluation to include multiple parameter classes. This extension leads to a more comprehensive assessment of satellite component health. Lastly, the method employs spectral clustering to determine the boundaries of different health status classes, offering an objective alternative to traditional expert-dependent approaches. By adopting this technique, the proposed method enhances the objectivity and accuracy of the health status classification. The experimental results show that the method is able to accurately describe the trends in the health status of components. Its effectiveness in real-time health assessment and monitoring of satellite components is confirmed. This research provides a valuable reference for further research on satellite component health assessment. It introduces novel and enhanced ideas and methodologies for practical applications.

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A bootstrapping-based weighted average asymptotic sampling formulation for reliability estimation of highly safe structures

Bayrak,

Nalkıran,

Acar

2023

Struct Multidisc Optim

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Small failure probability: principles, progress and perspectives

Cited by 9 publications

References 269 publications

AOK‐ES: Adaptive optimized Kriging combining efficient sampling for structural reliability analysis

AOK‐ES: Adaptive optimized Kriging combining efficient sampling for structural reliability analysis

A Novel Approach to Satellite Component Health Assessment Based on the Wasserstein Distance and Spectral Clustering

A bootstrapping-based weighted average asymptotic sampling formulation for reliability estimation of highly safe structures

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