...
  • 主题
高级搜索  >
历史搜索 清空历史
首页> 外文期刊>Proceedings of the Institution of Mechanical Engineers, Part O: Journal of Risk and Reliability >A multi-objective genetic algorithm for RAMS+C optimization with uncertain decision variables
【24h】

A multi-objective genetic algorithm for RAMS+C optimization with uncertain decision variables

机译：具有不确定决策变量的RAMS + C优化的多目标遗传算法

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

Surveillance requirements applied to safety systems of a nuclear power plant are established by the technical specification (TS). These requirements impose the surveillance test intervals (STIs) performed to ensure that safety-related systems normally in standby are ready to operate on demand. There is a great interest in the optimization of STIs, as they have a great effect on plant risk and also on the resources necessary to implement a certain surveillance test strategy. Operational experience at nuclear power plants shows that tests are not performed at a constant interval. Thus, it is a more realistic choice to consider the STI as a random variable rather than a constant value, as it has usually been considered. This paper focuses on the STI optimization based on risk and cost criteria and considering that the test intervals can be performed within a tolerance range.
机译：适用于核电厂安全系统的监视要求由技术规范（TS）制定。这些要求强加了执行的监视测试间隔（STI），以确保通常处于待机状态的安全相关系统可以按需运行。 STI的优化引起了极大的兴趣，因为它们对工厂风险以及实施某种监视测试策略所需的资源都有很大的影响。核电厂的运行经验表明，测试不是按固定间隔进行的。因此，将STI视为随机变量，而不是通常所考虑的恒定值是一个更现实的选择。本文重点介绍了基于风险和成本标准的STI优化，并考虑了测试间隔可以在公差范围内执行。

著录项

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