A fault diagnosis method based on signed directed graph and matrix for nuclear power plants

Liu, Yong-kuo; Wu, Guohua; Xie, Changsheng; Duan, Zhiyong; Peng, Minjun; Li, Mengkun

doi:10.1016/j.nucengdes.2015.11.016

Cited by 33 publications

(12 citation statements)

References 12 publications

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“…Lee and Shin (2018) proposed a method using time-frequency domain reflectometry and k-means clustering to determine the fault location and faulty line in a multi-core I&C cable system to assure the safety and reliability of NPP operation. Advances in qualitative causal graphs for NPP asset fault diagnosis include those on signed directed graphs (Liu et al, 2016) and dynamic uncertain causality graphs (Zhao et al, 2017a).…”

Section: Model-based Methodsmentioning

confidence: 99%

Prognostics and Health Management in Nuclear Power Plants: An Updated Method-Centric Review With Special Focus on Data-Driven Methods

et al. 2021

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In a carbon-constrained world, future uses of nuclear power technologies can contribute to climate change mitigation as the installed electricity generating capacity and range of applications could be much greater and more diverse than with the current plants. To preserve the nuclear industry competitiveness in the global energy market, prognostics and health management (PHM) of plant assets is expected to be important for supporting and sustaining improvements in the economics associated with operating nuclear power plants (NPPs) while maintaining their high availability. Of interest are long-term operation of the legacy fleet to 80 years through subsequent license renewals and economic operation of new builds of either light water reactors or advanced reactor designs. Recent advances in data-driven analysis methods—largely represented by those in artificial intelligence and machine learning—have enhanced applications ranging from robust anomaly detection to automated control and autonomous operation of complex systems. The NPP equipment PHM is one area where the application of these algorithmic advances can significantly improve the ability to perform asset management. This paper provides an updated method-centric review of the full PHM suite in NPPs focusing on data-driven methods and advances since the last major survey article was published in 2015. The main approaches and the state of practice are described, including those for the tasks of data acquisition, condition monitoring, diagnostics, prognostics, and planning and decision-making. Research advances in non-nuclear power applications are also included to assess findings that may be applicable to the nuclear industry, along with the opportunities and challenges when adapting these developments to NPPs. Finally, this paper identifies key research needs in regard to data availability and quality, verification and validation, and uncertainty quantification.

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Section: Model-based Methodsmentioning

confidence: 99%

Prognostics and Health Management in Nuclear Power Plants: An Updated Method-Centric Review With Special Focus on Data-Driven Methods

et al. 2021

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“…The first is based on the use of expert knowledge, such as expert systems (Kramer and Palowitch, 1987). The second is based on graph theory, that is, the model graphically displays relationships among the various parameters and faults as in a Bayesian network (Kang and Golay, 1999), a signed directed graph (Liu et al, 2016), and a dynamic uncertain causality graph (Zhou and Zhang, 2017).…”

Section: Introductionmentioning

confidence: 99%

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

Diping²,

Jiyao³

et al. 2021

Front. Energy Res.

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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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“…Commonly, the adopted solution to prevent possible fault is to implement an online monitoring and diagnosis when the nuclear reactor operating. With the signals in time domain under different conditions collected by the sensors, fault diagnosis methods are used to make recognition of the normal and fault conditions …”

Section: Introductionmentioning

confidence: 99%

Small‐batch‐size convolutional neural network based fault diagnosis system for nuclear energy production safety with big‐data environment

et al. 2020

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In nuclear energy production, with the continuous innovations and challenges in the big data and the industry 4.0 era, to guarantee the operation safety without the fault and failure will become more complex and intelligent. In this paper, a novel optimized convolutional neural network with small-batch-size processing (SCNN) was proposed and assembled in the nuclear fault diagnosis system. Eleven kinds of normal and fault conditions that include the whole 316 simulator sensor features were used to evaluate the performance of the proposed diagnosis system. The application of batch normalization with SCNN significantly optimized the model validation accuracy and loss under 100 epochs compared with normal operation and adding drop-out operation in same condition. Besides, outstanding diagnosis accuracy was highlighted by the comparison of traditional binary and multiple classification methods. This proposed diagnosis system has achieved more precise diagnosis accuracy and will provide the useful guidance to operators, assisting them to make accurate and rapid decision to ensure nuclear energy production safety. K E Y W O R D Sconvolution layer visualization, convolutional neural network, deep learning, fault diagnosis, nuclear energy production, small-batch-size processing

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A fault diagnosis method based on signed directed graph and matrix for nuclear power plants

Cited by 33 publications

References 12 publications

Prognostics and Health Management in Nuclear Power Plants: An Updated Method-Centric Review With Special Focus on Data-Driven Methods

Prognostics and Health Management in Nuclear Power Plants: An Updated Method-Centric Review With Special Focus on Data-Driven Methods

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

Small‐batch‐size convolutional neural network based fault diagnosis system for nuclear energy production safety with big‐data environment

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