A comparison of DBN model performance in SIPPRA health monitoring based on different data stream discretization methods
A comparison of DBN model performance in SIPPRA health monitoring based on different data stream discretization methods
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DOI：
10.1016/j.ress.2023.109206
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发表时间：
2023-02
期刊：
影响因子：
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通讯作者：
Austin D. Lewis;K. Groth
中科院分区：
文献类型：
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作者：
Austin D. Lewis;K. Groth
能源和工业部门依赖于核电厂或制造厂等复杂工程系统(CES)的可靠性；因此，重要的是监测系统健康状况，并就维护和风险管理做法做出明智的决定。一种建议的方法是使用基于因果的模型，如动态贝叶斯网络(DBN)，它包含工程系统内因果关系的结构逻辑并提供图形表示。当前CES建模的一个挑战是充分理解在开发底层条件概率表(CPT)时使用的不同数据流离散化对DBN系统健康估计的影响，该文论证了不同的时间离散化策略对为CES健康评估构建的DBN模型的性能的影响。利用钠快堆(SFR)经历瞬时过功率(TOP)的模拟核数据，采用不同的CES数据流离散化策略来构建基于健康的DBN模型的CPTS。这项研究发现，这些策略产生了不同的模型，具有不同的性能水平，用于确定对整体系统健康状况的不同评估。通过了解这些设计因素如何影响模型的健康评估，可以开发未来的风险模型，以提供对系统健康的更有意义的评估，从而做出更明智的决策。
The energy and industry sectors depend upon the reliability of complex engineering systems (CESes), such as nuclear power plants or manufacturing plants; it is important, therefore, to monitor system health and make informed decisions on maintenance and risk management practices. One proposed approach is to use causal-based models such as Dynamic Bayesian Networks (DBN), which contain the structural logic of and provide graphical representations of the causal relationships within engineering systems. A current challenge in CES modeling is fully understanding how different data stream discretizations used in developing underlying conditional probability tables (CPTs) impact the DBN’s system health estimates.This paper demonstrates the impact that different time discretization strategies have on the performance of DBN models built for CES health assessments. Using simulated nuclear data of a sodium fast reactor (SFR) experiencing a transient overpower (TOP), different strategies for discretizing CES data streams are used to construct the CPTs for a health-based DBN model. This study finds that these strategies generate different models with varying levels of performance for determining different assessments of overall system health. By understanding how these design factors impact the model’s health assessments, future risk models can be developed to provide a more meaningful assessment of a system’s health, resulting in more informed decisions.