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Boolean Network-Based Sensor Selection with Application to the Fault Diagnosis of a Nuclear Plant

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  • Zhe Dong

    (Institute of Nuclear and New Energy Technology, Collaborative Innovation Centre of Advanced Nuclear Energy Technology, Key Laboratory of Advanced Reactor Engineering and Safety of Ministry of Education, Tsinghua University, Beijing 100084, China)

Abstract

Fault diagnosis is crucial for the operation of energy systems such as nuclear plants, and heavily relies on various types of sensors for temperature, pressure, concentration, etc. Due to the redundancy of sensors in each energy system, the sensor selection scheme can deeply influence the diagnostic efficiency. In this paper, a Boolean network (BN) with its linear representation is proposed for describing the fault propagation among sensors. Both the sufficient condition of fault detectability and that of fault discriminability are given. Then, a sensor selection method for fault detection and discrimination is proposed. Finally, the theoretic result is applied to realize the diagnosis oriented sensor selection for a nuclear steam supply system based on a modular high temperature gas-cooled reactor (MHTGR). The computation and simulation results verify the correctness of the theoretical results.

Suggested Citation

  • Zhe Dong, 2017. "Boolean Network-Based Sensor Selection with Application to the Fault Diagnosis of a Nuclear Plant," Energies, MDPI, vol. 10(12), pages 1-13, December.
    • Handle: RePEc:gam:jeners:v:10:y:2017:i:12:p:2125-:d:122766
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    References listed on IDEAS

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    1. Dong, Zhe & Pan, Yifei & Zhang, Zuoyi & Dong, Yujie & Huang, Xiaojin, 2017. "Model-free adaptive control law for nuclear superheated-steam supply systems," Energy, Elsevier, vol. 135(C), pages 53-67.
    2. Zhe Dong & Yifei Pan & Zuoyi Zhang & Yujie Dong & Xiaojin Huang, 2017. "Modeling and Control of Fluid Flow Networks with Application to a Nuclear-Solar Hybrid Plant," Energies, MDPI, vol. 10(11), pages 1-21, November.
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    Cited by:

    1. Wang, Pengfei & Zhang, Jiaxuan & Wan, Jiashuang & Wu, Shifa, 2022. "A fault diagnosis method for small pressurized water reactors based on long short-term memory networks," Energy, Elsevier, vol. 239(PC).
    2. Wang, Yu & Yang, Yujing & Liu, Yang & Lou, Jungang, 2022. "Fault detection and pinning control of Boolean networks," Applied Mathematics and Computation, Elsevier, vol. 429(C).

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