A novel analysis method for vibration systems under time-varying uncertainties based on interval process model

Zhao, Gang; Wen, Guanghui; Liu, Jie

doi:10.1016/j.probengmech.2022.103363

Cited by 7 publications

(2 citation statements)

References 37 publications

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“…For the reliability of fusing fuzzy and interval variables, the existing research evidence on fuzzy theory mainly focuses on decision analysis and uncertain reasoning [33][34][35][36][37][38], whereas there is less research on reliability analysis. Gao and Zhang [39] evaluated the conservative value of structural reliability by calculating the worst reliability under the condition of interval variables and combining the information entropy method.…”

Section: Literature Reviewmentioning

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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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%

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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“…Therefore, a high-precision displacement prediction model should not be the end of the task, but rather a powerful tool to achieve it. Compared with the simple point prediction model, the interval prediction method will be more practicable [33,34].…”

Section: Introductionmentioning

confidence: 99%

A Combination Model for Displacement Interval Prediction of Concrete Dams Based on Residual Estimation

et al. 2022

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Accurate prediction and reasonable warning for dam displacement are important contents of dam safety monitoring. However, it is difficult to identify abnormal displacement based on deterministic point prediction results. In response, this paper proposes a model that integrates several strategies to achieve high-precision point prediction and interval prediction of dam displacement. Specifically, the interval prediction of dam displacement is realized in three stages. In the first stage, a displacement prediction model based on Extreme gradient boosting (XGBoost) is constructed. In the second stage, the prediction error sequence of XGBoost model is generated by the residual estimation method proposed in this paper, and the residual prediction model based on artificial neural network (ANN) is constructed through the maximum likelihood estimation method. In the third stage, the interval estimation of the noise sequence composed of the training error of the ANN model is carried out. Finally, the results obtained above are combined to realize the interval prediction of the dam displacement. The performance of the proposed model is verified by the monitoring data of an actual concrete dam. The results show that the hybrid model can not only achieve better point prediction accuracy than the single model, but also provide high quality interval prediction results.

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Mechanical properties of three-dimensional functionally graded triply periodic minimum surface structures

Qiu

Zhang

et al. 2023

International Journal of Mechanical Sciences

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A novel analysis method for vibration systems under time-varying uncertainties based on interval process model

Cited by 7 publications

References 37 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

A Combination Model for Displacement Interval Prediction of Concrete Dams Based on Residual Estimation

Mechanical properties of three-dimensional functionally graded triply periodic minimum surface structures

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