Abstract A fault diagnostic framework was investigated in this study for applications in thermal–hydraulic systems of nuclear power plants. The proposed framework consists of quantitative model-based diagnosis, statistical change detection and probabilistic reasoning. The use of physics-based diagnostic models provides high detection sensitivity and allows noise and measurement uncertainty to be incorporated robustly. Performance-related parametric models for each component are constructed based on first principles. Numerical model residuals are generated using the concept of analytical redundancy. Statistical change detection methods are employed to detect non-zero residuals in the presence of uncertainty. The diagnosis task is performed using Bayesian inference to detect and localize possible faults. Application to a single-phase heat exchanger for demonstration showed that the proposed probabilistic framework can provide improved results in comparison with traditional approaches while remaining less sensitive to false alarms in the presence of measurement and modeling uncertainty.

中文翻译：

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基于概率模型的核工程系统诊断框架

摘要 本研究研究了一种故障诊断框架，用于核电厂热工水力系统中的应用。所提出的框架由基于定量模型的诊断、统计变化检测和概率推理组成。基于物理的诊断模型的使用提供了高检测灵敏度，并允许稳健地合并噪声和测量不确定性。每个组件的性能相关参数模型都是基于第一原则构建的。使用分析冗余的概念生成数值模型残差。统计变化检测方法用于在存在不确定性的情况下检测非零残差。诊断任务使用贝叶斯推理来检测和定位可能的故障。