DOI QR코드

DOI QR Code

Parameter identifiability of Boolean networks with application to fault diagnosis of nuclear plants

  • Dong, Zhe (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) ;
  • Pan, Yifei (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) ;
  • Huang, Xiaojin (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)
  • 투고 : 2018.01.25
  • 심사 : 2018.03.08
  • 발행 : 2018.05.25

초록

Fault diagnosis depends critically on the selection of sensors monitoring crucial process variables. Boolean network (BN) is composed of nodes and directed edges, where the node state is quantized to the Boolean values of True or False and is determined by the logical functions of the network parameters and the states of other nodes with edges directed to this node. Since BN can describe the fault propagation in a sensor network, it can be applied to propose sensor selection strategy for fault diagnosis. In this article, a sufficient condition for parameter identifiability of BN is first proposed, based on which the sufficient condition for fault identifiability of a sensor network is given. Then, the fault identifiability condition induces a sensor selection strategy for sensor selection. Finally, the theoretical result is applied to the fault diagnosis-oriented sensor selection for a nuclear heating reactor plant, and both the numerical computation and simulation results verify the feasibility of the newly built BN-based sensor selection strategy.

키워드

참고문헌

  1. M. Bagajewicz, Design and retrofit of sensor networks in process plants, AIChE J. 43 (1997) 2300-2306. https://doi.org/10.1002/aic.690430915
  2. M. Bagajewicz, A review of techniques for instrumentation and upgrade in process plants, Can. J. Chem. Eng. 80 (2002) 3-16. https://doi.org/10.1002/cjce.5450800101
  3. M. Bagajewicz, E. Cabrera, New MILP formulation for instrumentation network design and upgrade, AIChE J. 48 (2002) 2271-2282. https://doi.org/10.1002/aic.690481017
  4. M. Bagajewicz, A. Fuxman, A. Uribe, Instrumentation network design and upgrade for process monitoring and fault detection, AIChE J. 50 (2004) 1870-1880. https://doi.org/10.1002/aic.10279
  5. M. Bhushan, S. Narasimhan, R. Rengaswamy, Robust sensor network design for fault diagnosis, Comp. Chem. Eng. 32 (2008) 1067-1084. https://doi.org/10.1016/j.compchemeng.2007.06.020
  6. S. Sen, S. Narasimhan, K. Deb, Sensor network design of linear processes using genetic algorithms, Comp. Chem. Eng. 22 (1998) 385-390.
  7. J.A. Carballido, I. Ponzoni, N.B. Brignole, CGD-GA: a graph-based genetic algorithm for sensor network design, Inform. Sci. 177 (2007) 5091-5102. https://doi.org/10.1016/j.ins.2007.05.036
  8. F. Li, B.R. Upadhyaya, Design of sensor placement for an integral pressurized water reactor using fault diagnostic observability and reliability criteria, Nucl. Technol. 173 (2011) 17-25. https://doi.org/10.13182/NT11-A11480
  9. F. Li, B.R. Upadhyaya, S.R.P. Perillo, Fault diagnosis of helical coil steam generator systems of an integral pressurized water reactor using optimal sensor selection, IEEE Trans. Nucl. Sci. 59 (2012) 403-410. https://doi.org/10.1109/TNS.2012.2185509
  10. S.M. Namburu, M.S. Azam, J. Luo, K. Choi, K.R. Pattipati, Data-driven modeling, fault diagnosis and optimal sensor selection for HVAC chillers, IEEE Trans. Autom. Sci. Eng. 4 (2007) 469-473. https://doi.org/10.1109/TASE.2006.888053
  11. N. Najjar, S. Gupta, J. Hare, S. Kandil, R. Walthall, Optimal sensor selection and fusion for heat exchanger fouling diagnosis in aerospace systems, IEEE Sens. J. 16 (2016) 4866-4881. https://doi.org/10.1109/JSEN.2016.2549860
  12. D. Li, Y. Zhou, G. Hu, C.J. Spanos, Optimal sensor configuration and feature selection for AHU fault detection and diagnosis, IEEE Trans. Ind. Inform. 13 (2017) 1369-1380. https://doi.org/10.1109/TII.2016.2644669
  13. S.A. Kauffman, Metabolic stability and epigenesist in randomly constructed genetic nets, J. Theor. Biol. 22 (1969) 437-467. https://doi.org/10.1016/0022-5193(69)90015-0
  14. C. Farrow, J. Heidel, H. Maloney, J. Rogers, Scalar equations for synchronous Boolean networks with biological applications, IEEE Trans. Neural Netw. 15 (2004) 348-354. https://doi.org/10.1109/TNN.2004.824262
  15. H. Qi, D. Cheng, Logic and logic-based control, J. Contr. Theory Appl. 6 (2008) 123-133.
  16. D. Cheng, H. Qi, Controllability and observability of Boolean control networks, Automatica 45 (2009) 1659-1667. https://doi.org/10.1016/j.automatica.2009.03.006
  17. D. Cheng, H. Qi, A linear representation of dynamics of Boolean networks, IEEE Trans. Automatic Contr. 55 (2010) 2251-2258. https://doi.org/10.1109/TAC.2010.2043294
  18. D. Cheng, H. Qi, State-space analysis of Boolean networks, IEEE Trans. Neural Netw. 55 (2010) 2251-2258.
  19. D. Wang, C. Ma, D. Dong, J. Lin, Chinese nuclear heating test reactor and demonstration plant, Nucl. Eng. Design 136 (1992) 91-98. https://doi.org/10.1016/0029-5493(92)90116-D
  20. D. Wang, The design characteristics and construction experiences of the 5 MW nuclear heating reactor, Nucl. Eng. Design 143 (1993) 19-24. https://doi.org/10.1016/0029-5493(93)90273-C
  21. W. Zheng, D. Wang, NHR-200 nuclear energy system and its possible applications, Progr. Nucl. Energy 29 (1995) 193-200.
  22. Z. Dong, Y. Pan, A lumped-parameter dynamical modal of a nuclear heating reactor cogeneration plant, Energy 145 (2018) 638-656. https://doi.org/10.1016/j.energy.2017.12.153

피인용 문헌

  1. A fuzzy fault accommodation method for nuclear power plants under actuator stuck faults vol.165, pp.None, 2018, https://doi.org/10.1016/j.anucene.2021.108674