结构可靠度分析中动态贝叶斯网络的有效离散化方法

IF 1.7 4区 工程技术 Q3 ENGINEERING, INDUSTRIAL
Hongseok Kim, Dooyoul Lee
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引用次数: 1

摘要

使用动态贝叶斯网络(DBN)来估计组件或系统随时间退化的失效风险有几个优点。DBN使变量的概率分布离散化,从而提高了计算资源的效率,减少了计算时间。然而,设计一个最优的离散化方案是很重要的,因为随着离散区间数量的增加,模型的大小呈指数增长。在本文中,我们提出了一种最优离散方案的DBN用于建模时变退化涡轮叶片部件。通过与蒙特卡罗仿真结果的比较,验证了用DBN估计可靠性指标的结果。此外,与对数变换离散化方法相比,DBN离散化方法的计算速度显著提高。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
An efficient discretization scheme for a dynamic Bayesian network in structural reliability analysis
Using a dynamic Bayesian network (DBN) to estimate the failure risk of a component or system that deteriorates with time has several advantages. A DBN discretizes the probability distribution of variables and thereby increases the efficiency of computing resources and reduces computation time. However, it is important to devise an optimal discretization scheme because the size of the model grows exponentially as the number of discretized intervals increases. In this paper, we propose an optimal discretization scheme for a DBN used to model the time-varying deterioration of a turbine blade component. The results of estimating the reliability indices with the DBN were verified by comparing them with the results of a Monte Carlo simulation. In addition, compared with a log-transformed discretization method, our DBN discretization method shows a significantly increased computation speed.
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来源期刊
CiteScore
4.50
自引率
19.00%
发文量
81
审稿时长
6-12 weeks
期刊介绍: The Journal of Risk and Reliability is for researchers and practitioners who are involved in the field of risk analysis and reliability engineering. The remit of the Journal covers concepts, theories, principles, approaches, methods and models for the proper understanding, assessment, characterisation and management of the risk and reliability of engineering systems. The journal welcomes papers which are based on mathematical and probabilistic analysis, simulation and/or optimisation, as well as works highlighting conceptual and managerial issues. Papers that provide perspectives on current practices and methods, and how to improve these, are also welcome
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