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Journal ArticleDOI

System reliability analysis based on dependent Kriging predictions and parallel learning strategy

TLDR
In this article, a new learning function with a parallel processing strategy is proposed for selecting new training samples for complex systems, which combines dependent Kriging predictions and parallel learning strategy to further improve the computational efficiency.
About
This article is published in Reliability Engineering & System Safety.The article was published on 2022-02-01. It has received 36 citations till now. The article focuses on the topics: Kriging & Computer science.

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Citations
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Journal ArticleDOI

Efficient local adaptive Kriging approximation method with single-loop strategy for reliability-based design optimization

TL;DR: In this paper , an efficient local adaptive Kriging approximation method with single-loop strategy (LAKAM-SLS) was proposed to enhance the computational efficiency of surrogate-based RBDO methods.
Journal ArticleDOI

Remaining Useful Life Estimation of Aircraft Engines Using a Joint Deep Learning Model Based on TCNN and Transformer

TL;DR: In this article, the authors proposed a joint deep learning architecture that is composed of two main parts: the transformer encoder, which uses scaled dot-product attention to extract dependencies across distances in time series, and the temporal convolution neural network (TCNN), which is constructed to fix the insensitivity of the self-attention mechanism to local features.
Journal ArticleDOI

An active learning reliability analysis method using adaptive Bayesian compressive sensing and Monte Carlo simulation (ABCS-MCS)

TL;DR: In this paper , an active learning reliability analysis method using adaptive Bayesian compressive sensing (ABCS) and MCS, denoted ABCS-MCS, was developed to improve the efficiency of reliability analysis for computationally timeconsuming systems.
Journal ArticleDOI

Probabilistic deep learning methodology for uncertainty quantification of remaining useful lifetime of multi-component systems

TL;DR: In this paper , a probabilistic deep leaning methodology for uncertainty quantification of multi-component systems' RUL is presented, which is a combination of a probablistic model and a deep recurrent neural network to predict the component's RUL distributions.
Journal ArticleDOI

Kinematic trajectory accuracy reliability analysis for industrial robots considering intercorrelations among multi-point positioning errors

TL;DR: In this article , the authors proposed a new kinematic trajectory accuracy reliability analysis method for industrial robots by integrating the sparse grid technique, the saddlepoint approximation method and copula functions.
References
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MonographDOI

Engineering Design via Surrogate Modelling

TL;DR: In this article, the authors propose a sampling approach to estimate the distribution of elementary effects and then use this information to construct a kriging model of the data set, which is then used for regression.
Journal ArticleDOI

AK-MCS: An active learning reliability method combining Kriging and Monte Carlo Simulation

TL;DR: An iterative approach based on Monte Carlo Simulation and Kriging metamodel to assess the reliability of structures in a more efficient way and is shown to be very efficient as the probability of failure obtained with AK-MCS is very accurate and this, for only a small number of calls to the performance function.
Journal ArticleDOI

Narrow Reliability Bounds for Structural Systems

Ove Ditlevsen
TL;DR: In this article, a simple geometrical interpretation of the correlation between mode safety margins combined with a well-known geometry interpretation of single mode reliability index makes the practical calculation of the system reliability bounds easy, particularly when the set of basic variables is jointly normally distributed.
Book

Programming the finite element method

TL;DR: This third edition of this highly-successful book is completely revised, and has all programs and subroutine libraries written in FORTRAN 90.
Journal ArticleDOI

Efficient Global Reliability Analysis for Nonlinear Implicit Performance Functions

TL;DR: This paper develops an efficient reliability analysis method that accurately characterizes the limit state throughout the random variable space and is both accurate for any arbitrarily shaped limit state and computationally efficient even for expensive response functions.
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