Adaptive kriging-based efficient reliability method for structural systems with multiple failure modes and mixed variables

Xiao, Ning‐Cong; Yuan, Kai; Zhou, Chengning

doi:10.1016/j.cma.2019.112649

Cited by 110 publications

(25 citation statements)

References 45 publications

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“…To improve the efficiency of RBDO with computationally time-consuming and expensive constraints, the metamodel-assisted approaches have attracted considerable attention, in which many kinds of metamodels are employed, such as response surface, 49 artificial neural network, 50 support vector machine, 51 and Kriging. [52][53][54][55][56] Among these metamodels, Kriging has extensive applications in RBDO. For RBDO under only random variables, some sampling methods have been developed for Kriging update, such as constraint boundary sampling, 57 local adaptive sampling, 58 important boundary sampling, 59 and adaptive directional boundary sampling.…”

Section: Introductionmentioning

confidence: 99%

A new hybrid reliability‐based design optimization method under random and interval uncertainties

Zhang

Gao

Xiao

2020

Numerical Meth Engineering

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Summary This article proposes a new method for hybrid reliability‐based design optimization under random and interval uncertainties (HRBDO‐RI). In this method, Monte Carlo simulation (MCS) is employed to estimate the upper bound of failure probability, and stochastic sensitivity analysis (SSA) is extended to calculate the sensitivity information of failure probability in HRBDO‐RI. Due to a large number of samples involved in MCS and SSA, Kriging metamodels are constructed to substitute true constraints. To avoid unnecessary computational cost on Kriging metamodel construction, a new screening criterion based on the coefficient of variation of failure probability is developed to judge active constraints in HRBDO‐RI. Then a projection‐outline‐based active learning Kriging is achieved by sequentially select update points around the projection outlines on the limit‐state surfaces of active constraints. Furthermore, the prediction uncertainty of Kriging metamodel is quantified and considered in the termination of Kriging update. Several examples, including a piezoelectric energy harvester design, are presented to test the accuracy and efficiency of the proposed method for HRBDO‐RI.

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Section: Introductionmentioning

confidence: 99%

A new hybrid reliability‐based design optimization method under random and interval uncertainties

Zhang

Gao

Xiao

2020

Numerical Meth Engineering

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“…The LS-based method in the work by Ditlevsen et al 23 introduced a hypothesis that ''the important directions are not affected by the distribution parameters.'' Recently, adaptive strategy of surrogate model coupling with simulation methods have been developed for reliability analysis [29][30][31] and these surrogate model methods can also be easily extended for LRS.…”

Section: Introductionmentioning

confidence: 99%

“…The LS-based and DS-based methods in previous literature 26–28 have analogous idea to determine the partial derivatives between a series of “reliability index” and distribution characteristics. The LS-based method in the work by Ditlevsen et al 23 introduced a hypothesis that “the important directions are not affected by the distribution parameters.” Recently, adaptive strategy of surrogate model coupling with simulation methods have been developed for reliability analysis 29–31 and these surrogate model methods can also be easily extended for LRS.…”

Section: Introductionmentioning

confidence: 99%

A novel reliability sensitivity analysis method based on directional sampling and Monte Carlo simulation

Zhang

Lü

Cheng

et al. 2020

Proceedings of the Institution of Mechanical Engineers, Part O:

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Local reliability sensitivity and global reliability sensitivity are required in reliability-based design optimization, since they can provide rich information including variable importance ranking and gradient information. However, traditional Monte Carlo simulation is inefficient for engineering application. A novel numerical simulation method based on Monte Carlo simulation and directional sampling is proposed to simultaneously estimate local reliability sensitivity and global reliability sensitivity. By suitable transformation, local reliability sensitivity and global reliability sensitivity can be estimated simultaneously as by-products of reliability analysis for Monte Carlo simulation method. The key is how to efficiently classify Monte Carlo simulation samples into two categories: failure samples and safety samples. Directional sampling method, a classical reliability analysis method, is more efficient than Monte Carlo simulation for reliability analysis. A novel strategy based on nearest Euclidean distance is proposed to approximately screen out failure samples from Monte Carlo simulation samples using directional sampling samples. In the proposed method, local reliability sensitivity and global reliability sensitivity are by-products of reliability analysis using the directional sampling method. Different from existing methods, the proposed method does not introduce hypotheses and does not require additional gradient information. The advantages of the Monte Carlo simulation and directional sampling are well integrated in the proposed method. The accuracy and the efficiency of the proposed method for local reliability sensitivity and global reliability sensitivity are demonstrated by four numerical examples and two engineering examples including the headless rivet and the wing box structure.

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“…Huang et al [14] analyzed the reliability of the kinematic accuracy of gear mechanisms using the presented method and explored the influences of original errors on the transmission error of a gear mechanism. Xiao et al [15] proposed a reliability analysis method for structural systems with multiple failure modes and mixed variables, which is suitable for complex systems. Nejad et al [16] calculated the long-term fatigue damage of the gear tooth and analyzed the reliability of the geared transmission system using the first-order reliability method (FORM).…”

Section: Introductionmentioning

confidence: 99%

Fatigue Reliability Analysis of Wind Turbine Drivetrain Considering Strength Degradation and Load Sharing Using Survival Signature and FTA

Zhu

Chen

et al. 2020

Energies

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The wind turbine drivetrain suffers significant impact loads that severely affect the reliability and safety of wind turbines. Bearings and gears within the drivetrain are critical components with high repair costs and lengthy downtime. To realistically assess the system reliability, we propose to establish an electromechanical coupling dynamic model of the wind turbine considering the control strategy and environmental parameters and evaluate the system’s reliability of wind turbine drivetrain based on loads of gears and bearings. This paper focuses on the dynamic reliability analysis of the wind turbine under the control strategy and environmental conditions. SIMPACK (v9.7, Dassault Systèmes, Gilching, Germany) is used to develop the aero-hydro-servo-elastic coupling dynamic model with the full drivetrain that considers the flexibility of the tower and blade, the stochastic loads of wind and waves, gear meshing features, as well as the control strategy. The system reliability level of wind turbine drivetrain at different wind conditions is assessed using survival signature and fault tree analysis (FTA), and the influences of strength degradation of the transmission components on the system reliability are explored. Following this, the bending fatigue reliability and contact fatigue reliability concerning different wind conditions are compared in this paper. A case study is performed to demonstrate the effectiveness and feasibility of the proposed methodology.

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Adaptive kriging-based efficient reliability method for structural systems with multiple failure modes and mixed variables

Cited by 110 publications

References 45 publications

A new hybrid reliability‐based design optimization method under random and interval uncertainties

A new hybrid reliability‐based design optimization method under random and interval uncertainties

A novel reliability sensitivity analysis method based on directional sampling and Monte Carlo simulation

Fatigue Reliability Analysis of Wind Turbine Drivetrain Considering Strength Degradation and Load Sharing Using Survival Signature and FTA

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