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首页> 外文会议>International topical meeting on nuclear plant instrumentation, control, and human-machine interface technologies >ADDRESSING UNCERTAINTY IN PREDICTIVE ESTIMATES OF RISK
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ADDRESSING UNCERTAINTY IN PREDICTIVE ESTIMATES OF RISK

机译：预测风险中的不确定性处理

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Probabilistic risk assessment (PRA) provides a static representation of risk associated with operations and maintenance (O&M) of nuclear power plants. Generic aging models can be used to predict the associated risk over time based on the assumed aging characteristics of specific components. These methods do not take into account the current condition of the components, and are susceptible to error in probabilistic estimates. Enhanced risk monitors (ERMs) use component condition (based on condition monitoring data) and time-dependent failure probabilities from prognostic health management (PHM) systems to calculate the risk associated with continued operation using potentially degraded components. ERMs are likely to be of value with advanced reactors, given the relative lack of operational data and the potential need to inform design choices and O&M actions to optimize plant performance, economics, and safety. Using equipment condition assessment methods to gather real-time conditions of advanced reactor components provides more accurate data to input into risk assessments within the ERM framework. This time-dependent condition data can be used as inputs for aging models in order to forecast the probabilistic risk associated with O&M. Such data is subject to uncertainties from measurements and historical operational conditions, along with uncertainties in aging models for components, resulting in uncertainty in estimates of component condition and predicted risk. Periodic updates with real-time measurements may be used to mitigate some uncertainty, which will need to be quantified. This paper describes the basic methodologies for incorporating periodic equipment condition monitoring data into an aging model to provide a forecasted risk assessment for prototypical advanced reactor components. This ERM methodology will include methods for propagating uncertainty over time within a dynamic predictive risk assessment that accounts for multiple interconnected components and failure events. Results of applying the ERM for a simplified advanced reactor PRA model will be presented.
机译：概率风险评估（PRA）可静态表示与核电厂的运营和维护（O＆M）相关的风险。通用老化模型可用于基于特定组件的假定老化特征来预测随时间推移的相关风险。这些方法未考虑组件的当前状况，并且容易出现概率估计中的错误。增强型风险监控器（ERM）使用组件状况（基于状况监控数据）和预后健康管理（PHM）系统中与时间相关的故障概率来计算与使用潜在降级组件的持续操作相关的风险。鉴于相对缺乏运行数据以及潜在需要告知设计选择和运维措施以优化工厂性能，经济性和安全性，ERM在先进反应堆中可能具有价值。使用设备状态评估方法来收集先进反应堆组件的实时状态可以提供更准确的数据，以输入到ERM框架内的风险评估中。此时间相关的条件数据可用作老化模型的输入，以便预测与运维相关的概率风险。此类数据受测量和历史操作条件的不确定性以及零部件老化模型的不确定性的影响，从而导致零部件状态和预计风险的估计存在不确定性。具有实时测量值的定期更新可用于减轻某些不确定性，这需要量化。本文介绍了将定期设备状态监测数据纳入老化模型以为典型先进反应堆组件提供预测风险评估的基本方法。这种ERM方法论将包括在动态预测风险评估中随着时间传播不确定性的方法，该评估考虑了多个相互关联的组件和故障事件。将介绍将ERM用于简化的先进反应堆PRA模型的结果。

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