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Application of monitoring and prognostics to small modular reactors

机译:监测和预测在小型模块化反应堆中的应用

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Prognostics is one component of a full health monitoring system, which generally includes plant condition monitoring, fault detection, fault diagnostics, and estimation of remaining useful life. Empirical methods for prognostics have been widely studied, and the efficacy of these models is well accepted. However, these methods typically require large amounts of run-to-failure data for accurate model development. This has been one of the main roadblocks for developing prognostic models for high reliability or safety critical systems: equipment rarely malfunctions; when it does, it is often times repaired before failure occurs. This paper presents the work completed in developing and validating an accurate prognostic system for several potential IRIS system fault modes using high-fidelity simulated data before any plant operation takes place. Fault modes under investigation include heat exchanger fouling, sensor calibration drifts, and pump degradation. Prognostic models are developed which can be applied to the IRIS plant from day one of operation to facilitate the final step in a full health monitoring system. These estimates of remaining useful life can be used to inform both operations and maintenance planning to reduce plant downtime and provide the highest level of power production from the beginning of operation.
机译:预测是整个健康监控系统的一个组成部分,该系统通常包括工厂状态监控,故障检测,故障诊断以及剩余使用寿命的估算。预后的经验方法已经被广泛研究,并且这些模型的有效性已被广泛接受。但是,这些方法通常需要大量运行失败数据才能进行精确的模型开发。这一直是开发用于高可靠性或安全性至关重要的系统的预测模型的主要障碍之一:设备很少出现故障;当发生故障时,通常会在故障发生之前对其进行维修。本文介绍了在进行任何工厂运营之前,使用高保真模拟数据为几种潜在的IRIS系统故障模式开发和验证准确的预后系统所完成的工作。正在研究的故障模式包括热交换器结垢,传感器校准偏差和泵性能下降。开发了预后模型,可从运行的第一天起将其应用于IRIS工厂,以促进完整健康监控系统的最后一步。这些剩余使用寿命的估计值可用于通知操作和维护计划,以减少工厂停机时间,并从操作开始就提供最高水平的电力生产。

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