A Fault Diagnosis Method Based on Signed Directed Graph and Correlation Analysis for Nuclear Power Plants

Abstract Fault detection and diagnosis (FDD) provides safety alarms and diagnostic functions for a nuclear power plant (NPP), which comprises large and complex systems. NPP has a large number of parameters which make it difficult achieve FDD. Now many diagnosis methods have lack of better explanation for faults and quantitative analysis. Therefore, to overcome the “black box” of FDD based on data-driven methods, this paper adopts signed directed graph (SDG) in knowledge graph for FDD. It can express the cause and effect of accidents through knowledge maps. At same time, this paper uses correlation analysis to conduct a quantitative analysis between parameters and faults. It this paper, SDG is used to explain the reason of faults. In order to quickly achieve FDD, this paper introduces a quantitative analysis method. It combines expert system and correlation analysis method to analyze the weight of each parameter. On this basis, matrix reasoning is used to achieve the FDD, and the reason is shown in SDG model inference. This paper takes loss of coolant accident as the case study, the case shows that the proposed method is superior to the conventional SDG method and can diagnose the faults timely.

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Online Fault Diagnosis of Nuclear Power Plants Using Signed Directed Graph and Fuzzy Theory

Volume 4: Nuclear Safety, Security, Non-Proliferation and Cyber Security; Risk Management ◽

10.1115/icone25-66367 ◽

2017 ◽

Author(s):

Guohua Wu ◽

Liguo Zhang ◽

Jiejuan Tong

Keyword(s):

Directed Graph ◽

Nuclear Power ◽

Power Plants ◽

Fuzzy Theory ◽

Fault Detection And Diagnosis ◽

Propagation Path ◽

Simulation Experiments ◽

Safety Standards ◽

Signed Directed Graph ◽

The Status

When nuclear power plant (NPPs) is in fault, it may release radioactivity into the environment. Therefore, extremely high safety standards specification are required during its working. So it is critically important for fault detection and diagnosis (FDD). NPPs are composed of large and complex systems, it is of great significance to obtain the up-to-date information of NPPs’ running state. So FDD is used to provide the state of system accurately and timely in NPPs. Signed directed graph (SDG) can show the complex relationship between parameters and has advantages of conveniently modeling, flexible inference and so on, so SDG is adopted for FDD. To achieve SDG inference better, fuzzy theory is utilized for signal processing in the paper. Firstly, SDG model is built according to the basic steps and principles of SDG modeling, and the parameters are divided into three states which is monitored by fuzzy theory. Secondly, according to the status of parameters, SDG is used for FDD and to reveal the fault propagation path, thus possibility of each fault occurred is achieved. Finally, to verify the validity of the method, the simulation experiments are done for NPPs and the simulation experiments show that SDG-fuzzy theory framework for FDD can get the fault possibility and deeply explain the reasons of fault.

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A Framework for Monitoring and Fault Diagnosis in Nuclear Power Plants Based on Signed Directed Graph Methods

Frontiers in Energy Research ◽

10.3389/fenrg.2021.641545 ◽

2021 ◽

Vol 9 ◽

Author(s):

Wu Guohua ◽

Yuan Diping ◽

Yin Jiyao ◽

Xiao Yiqing ◽

Ji Dongxu

Keyword(s):

Directed Graph ◽

Process Monitoring ◽

Nuclear Power ◽

Power Plants ◽

Nuclear Power Plants ◽

High Standard ◽

Fault Detection And Diagnosis ◽

Radioactive Material ◽

Feed Water ◽

Signed Directed Graph

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.

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Research on Fault Diagnosis Based on Hierarchical Signed Directed Graph for Nuclear Power Plants

Volume 2: Nuclear Policy; Nuclear Safety, Security, and Cyber Security; Operating Plant Experience; Probabilistic Risk Assessments; SMR and Advanced Reactors ◽

10.1115/icone2020-16192 ◽

2020 ◽

Author(s):

Wu Guohua ◽

Wang Jiaxin ◽

Yuan Diping ◽

Xiao Yiqing

Keyword(s):

Fault Diagnosis ◽

Directed Graph ◽

Nuclear Power ◽

Power Plants ◽

Nuclear Power Plants ◽

Layer Structure ◽

Failure Process ◽

High Standard ◽

Fault Detection And Diagnosis ◽

Signed Directed Graph

Abstract When nuclear power plants (NPPs) are in failure state, it may release radioactive substance into the environment. Thus, the safety of NPPs is put forward a high standard. Fault detection and diagnosis (FDD) are significant for NPPs to help operator timely know the state of system and provide the online guidance. Fault diagnosis can improve the safety of nuclear power plants, but current fault diagnosis methods pay too much attention to accuracy of diagnostic results. As a complex industrial system, how to explain the causes of faults in NPPs becomes more important. Although there are many studies on the knowledge graph, there is no detailed description on the failure process (consider timing). This paper proposed a three-layer structure for FDD in NPPs. Each layer represents the stage of the accident, it can give the operator a clear cognitive process to faults. The three-layer structure includes “smooth layer”, “threshold layer” and “fault layer”. The three layers indicate the reason of faults, the response of the parameters at each stage, and clearly showing the accident process. The smooth layer uses the stability analysis to analyze whether the current NPP is operating abnormally; the threshold layer uses the thresholds of the NPP to monitor which parameters have exceeded the upper limit or the lower limit; the fault layer reflects what is happening in the current operation and accidents are explained using signed directed graph. This paper takes the loss of coolant accident as an example, three-layer structure is analyzed, which shows the feasibility of the method. The case shows that the proposed method is superior to the conventional SDG method, can diagnose the faults, and give the reason of diagnosis results.

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