Abstract
When nuclear power plants (NPPs) are in a state of failure, they may release radioactive material into the environment. The safety of NPPs must thus be maintained at a high standard. Online monitoring and fault detection and diagnosis (FDD) are important in helping NPP operators understand the state of the system and provide online guidance in a timely manner. Here, to mitigate the shortcomings of process monitoring in NPPs, five-level threshold, qualitative trend analysis (QTA), and signed directed graph (SDG) inference are combined to improve the veracity and sensitivity of process monitoring and FDD. First, a three-level threshold is used for process monitoring to ensure the accuracy of an alarm signal, and candidate faults are determined based on SDG backward inference from the alarm parameters. According to the candidate faults, SDG forward inference is applied to obtain candidate parameters. Second, a five-level threshold and QTA are combined to determine the qualitative trend of candidate parameters to be utilized for FDD. Finally, real faults are identified by SDG forward inference on the basis of alarm parameters and the qualitative trend of candidate parameters. To verify the validity of the method, we have conducted simulation experiments, which comprise loss of coolant accident, steam generator tube rupture, loss of feed water, main steam line break, and station black-out. This case study shows that the proposed method is superior to the conventional SDG method and can diagnose faults more quickly and accurately.
Highlights
Nuclear power plants (NPPs) are large and complex systems
Section introduces the signed directed graph (SDG) method; Process Monitoring for Nuclear Power Plants Section presents the method of process monitoring; and Monitoring and Fault Diagnosis Framework for Nuclear Power Plants Section proposes a combination of five-level threshold, qualitative trend analysis (QTA), and SDG inference
The three-level threshold is initially applied for process monitoring, and when alarm signals appear as defined by the three-level threshold, candidate faults are identified by SDG backward inference
Summary
Nuclear power plants (NPPs) are large and complex systems. To ensure the reliability and safety of NPPs, process monitoring and fault detection and diagnosis (FDD) are implemented to provide online guidance for operators diagnosing the abnormal functioning of NPPs in an accurate and timely manner (Liu et al, 2013; Liu et al, 2014). Compared with other data-driven methods, SDG has the significant advantage that SDG-based FDD can reveal fault propagation paths and comprehensively explain causes of failure (Chen et al, 2015; Maurya et al, 2004), which has led to it becoming widely implemented in industry. The safety threshold in NPPs is very conservative, which increases the difficulty of applying FDD and makes incipient fault diagnosis difficult (Chung and Bien, 1994) To solve these problems, SDG combined with principal component analysis was proposed for FDD, and principal component analysis was applied to solve the threshold problem in process monitoring. According to the alarm parameters h, d, f, candidate faults are identified based on backward inference and identifying a consistent path. Backward inference generally starts from the sign nodes back to the fault nodes based on a consistent path and is used for FDD (Mano et al, 2006). The method of process monitoring is based on threshold and QTA methods
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