• 主题
高级搜索  >
历史搜索 清空历史
首页> 外文期刊>Reliability Engineering & System Safety >Scenario clustering and dynamic probabilistic risk assessment
【24h】

Scenario clustering and dynamic probabilistic risk assessment

机译：场景聚类和动态概率风险评估

获取原文
获取原文并翻译 | 示例
           
页面导航
  • 摘要
  • 著录项
  • 相似文献
  • 相关主题

摘要

A challenging aspect of dynamic methodologies for probabilistic risk assessment (PRA), such as the Dynamic Event Tree (DET) methodology, is the large number of scenarios generated for a single initiating event. Such large amounts of information can be difficult to organize for extracting useful information. Furthermore, it is not often sufficient to merely calculate a quantitative value for the risk and its associated uncertainties. The development of risk insights that can increase system safety and improve system performance requires the interpretation of scenario evolutions and the principal characteristics of the events that contribute to the risk. For a given scenario dataset, it can be useful to identify the scenarios that have similar behaviors (i.e., identify the most evident classes), and decide for each event sequence, to which class it belongs (i.e., classification). It is shown how it is possible to accomplish these two objectives using the Mean-Shift Methodology (MSM). The MSM is a kernel-based, non-parametric density estimation technique that is used to find the modes of an unknown data distribution. The algorithm developed finds the modes of the data distribution in the state space corresponding to regions with highest data density as well as grouping the scenarios generated into clusters based on scenario temporal similarities. The MSM is illustrated using the data generated by a DET algorithm for the analysis of a simple level/temperature controller and reactor vessel auxiliary cooling system.
机译：诸如风险事件树（DET）方法之类的用于概率风险评估（PRA）的动态方法论的一个具有挑战性的方面是，为单个启动事件生成了大量的场景。如此大量的信息可能难以组织以提取有用的信息。此外，仅仅计算风险及其相关不确定性的定量值通常并不足够。可以提高系统安全性和改善系统性能的风险洞察力的发展，需要对场景演变和造成风险的事件的主要特征进行解释。对于给定的场景数据集，识别具有相似行为的场景（即标识最明显的类别），并为每个事件序列决定其所属的类别（即分类）可能很有用。展示了如何使用均值漂移方法（MSM）来实现这两个目标。 MSM是基于内核的非参数密度估计技术，用于发现未知数据分布的模式。开发的算法找到状态空间中与数据密度最高的区域相对应的数据分布模式，并根据场景时间相似度将生成的场景分组为聚类。使用DET算法生成的数据说明了MSM，以分析简单的液位/温度控制器和反应堆容器辅助冷却系统。

著录项

  • 来源
    《Reliability Engineering & System Safety》 |2013年第7期|146-160|共15页
  • 作者

    Diego Mandelli; Alper Yilmaz; Tunc Aldemir; Kyle Metzroth; Richard Denning;

  • 作者单位

    Nuclear Engineering Program, The Ohio State University, Columbus, OH, United States;

    Photogrammetric Computer Vision Lab., The Ohio State University, Columbus, OH, United States;

    Nuclear Engineering Program, The Ohio State University, Columbus, OH, United States;

    Nuclear Engineering Program, The Ohio State University, Columbus, OH, United States;

    Nuclear Engineering Program, The Ohio State University, Columbus, OH, United States;

  • 收录信息 美国《科学引文索引》(SCI);美国《工程索引》(EI);
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

    Transient analysis; Scenario clustering; Dynamic PRA;

    机译：瞬态分析;场景聚类;动态PRA;

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱：kefu@zhangqiaokeyan.com

京公网安备：11010802029741号 ICP备案号：京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有

  • 客服微信

  • 服务号