Structural hybrid reliability index and its convergent solving method based on random–fuzzy–interval reliability model

An, Honglei; Zhou, Lei; Sun, Hui

doi:10.1177/1687814016665798

Cited by 7 publications

(4 citation statements)

References 27 publications

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“…When all three types of uncertainty exist, most processing methods are based on adding a third uncertainty variable into the method of fusing two uncertain variables. In [43], fuzzy variables and random variables were transformed into interval variables and then solved by an iterative first-order reliability method. In [44], random variables and fuzzy variables (equivalent to random variables) were discretized into subintervals, and finally, the reliability analysis was completed by using evidence theory.…”

Section: Literature Reviewmentioning

confidence: 99%

“…An et al [43] Iterative first-order reliability method Tang et al [44] Subintervals and evidence theory Zhang et al [45] Uniformity variable conversion 3 International Journal of Energy Research into random variables and interval variables into random variables by the uniformity method and then used the FORM method to solve the structural reliability.…”

Section: Random and Fuzzy And Intervalmentioning

confidence: 99%

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A Multisource Uncertainty Fusion Reliability Evaluation Method for the Control Rod Drive Mechanism of Nuclear Power Plants

Gao,

Sun,

et al. 2024

International Journal of Energy Research

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The reliability of a pressurized water reactor power plant’s control rod drive mechanism (CRDM) is affected by many factors, such as operation states, unit performance, and dynamic environments. Multiple sources of uncertainties, including random, interval, and fuzzy, exist when analyzing the reliability of CRDMs. Modeling reliability without considering the fusion of multisource uncertainties may result in model distortion and may not provide realistic assessment results. This paper proposes a multisource uncertainty fusion method powered by the margin-based variable transformation and the Bayesian network propagation. The interval and fuzzy variables are transformed into random variables to obtain the corresponding generalized probability density function of the CRDM’s feature parameters. After that, the CRDM’s reliability can be evaluated via probability quantization of the Bayesian network inference result through sampling algorithms. A magnetic-jack type CRDM case is presented to verify the proposed method, and the results show that this method can fuse three types of uncertainty variable in a unified way and effectively obtain reliability evaluation results.

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Section: Literature Reviewmentioning

confidence: 99%

Section: Random and Fuzzy And Intervalmentioning

confidence: 99%

A Multisource Uncertainty Fusion Reliability Evaluation Method for the Control Rod Drive Mechanism of Nuclear Power Plants

Gao,

Sun,

et al. 2024

International Journal of Energy Research

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“…Nevertheless, for a problem that contains both probabilistic and nonprobabilistic variables, the probabilistic and nonprobabilistic hybrid reliability method can take full advantage of the known information to achieve an effective analysis. Many scholars have studied probabilistic and nonprobabilistic hybrid reliability analysis (HRA) [4][5][6][7][8][9][10][11][12][13][14][15][16].…”

Section: Introductionmentioning

confidence: 99%

Application of Probabilistic and Nonprobabilistic Hybrid Reliability Analysis Based on Dynamic Substructural Extremum Response Surface Decoupling Method for a Blisk of the Aeroengine

Bai

Zhang

et al. 2017

International Journal of Aerospace Engineering

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For the nondeterministic factors of an aeroengine blisk, including both factors with sufficient and insufficient statistical data, based on the dynamic substructural method of determinate analysis, the extremum response surface method of probabilistic analysis, and the interval method of nonprobabilistic analysis, a methodology called the probabilistic and nonprobabilistic hybrid reliability analysis based on dynamic substructural extremum response surface decoupling method (P-NP-HRA-DS-ERSDM) is proposed. The model includes random variables and interval variables to determine the interval failure probability and the interval reliability index. The extremum response surface function and its flow chart of mixed reliability analysis are given. The interval analysis is embedded in the most likely failure point in the iterative process. The probabilistic analysis and nonprobabilistic analysis are investigated alternately. Tuned and mistuned blisks are studied in a complicated environment, and the results are compared with the Monte Carlo method (MCM) and the multilevel nested algorithm (MLNA) to verify that the hybrid model can better handle reliability problems concurrently containing random variables and interval variables; meanwhile, it manifests that the computational efficiency of this method is superior and more reasonable for analysing and designing a mistuned blisk. Therefore, this methodology has very important practical significance.

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“…Balu et al [34] studied the reliability analysis problem with both random and fuzzy variables based on Fourier transform. An et al [35] presented a new hybrid reliability index and its solving method based on random-fuzzy-interval model. Ni et al [36] established a new hybrid reliability model which contains randomness, fuzziness, and nonprobabilistic uncertainties based on the structural fuzzy random reliability and nonprobabilistic setbased models.…”

Section: Introductionmentioning

confidence: 99%

Discretization Analysis Method of Hybrid Reliability Based on Evidence Theory

Tang

2018

Mathematical Problems in Engineering

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Aiming at the problem that various types of uncertainties, such as randomness, fuzziness, and interval, coexist in structure reliability analysis, a discretization analysis method of hybrid reliability for uncertain structures is proposed based on evidence theory (ET) in this article. Firstly, in order to establish a hybrid reliability model based on ET, a generalized density method (GDM) is developed to transform the fuzzy variables into equivalent random variables on the basis of the entropy equivalent method (EEM). Based on the discrete property of the basic probability assignment (BPA) in evidence theory, the random variables and fuzzy variables (equivalent random variables) are both discretized into subintervals according to six-sigma rule. Then, the BPA of each subinterval is solved and all focal elements are assigned BPA, so the evidence structure characterization of random and fuzzy variables is realized. Secondly, using Fmincon function based on the sequential quadratic programming (SQP) algorithm in MATLAB, the minimum and maximum values of performance function over each focal element can be acquired directly. Meanwhile, the production rules are used to judge the belonging of focal elements and classify them, so the numerical calculation of belief measure and plausibility measure is also realized. Finally, combined with the Monte Carlo Simulation (MCS) method, an engineering example is provided to demonstrate the feasibility and accuracy of the proposed method.

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Structural hybrid reliability index and its convergent solving method based on random–fuzzy–interval reliability model

Cited by 7 publications

References 27 publications

A Multisource Uncertainty Fusion Reliability Evaluation Method for the Control Rod Drive Mechanism of Nuclear Power Plants

A Multisource Uncertainty Fusion Reliability Evaluation Method for the Control Rod Drive Mechanism of Nuclear Power Plants

Application of Probabilistic and Nonprobabilistic Hybrid Reliability Analysis Based on Dynamic Substructural Extremum Response Surface Decoupling Method for a Blisk of the Aeroengine

Discretization Analysis Method of Hybrid Reliability Based on Evidence Theory

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