DOI QR코드

DOI QR Code

Research on aging-related degradation of control rod drive system based on dynamic object-oriented Bayesian network and hidden Markov model

  • Kang Zhu (Institute of Nuclear Science and Technology, Naval University of Engineering) ;
  • Xinwen Zhao (Institute of Nuclear Science and Technology, Naval University of Engineering) ;
  • Liming Zhang (Institute of Nuclear Science and Technology, Naval University of Engineering) ;
  • Hang Yu (Institute of Nuclear Science and Technology, Naval University of Engineering)
  • Received : 2022.02.03
  • Accepted : 2022.06.19
  • Published : 2022.11.25

Abstract

The control rod drive system is critical to the reactor's reliable operation. The performance of its control system and mechanical system will gradually deteriorate because of operational and environmental stresses, thus increasing the reactor's operational risk. Currently there are few researches on the aging-related degradation of the entire control rod drive system. Because it is difficult to quantify the effect of various environmental stresses and establish an accurate physical model when multiple mechanisms superimposed in the degradation process. Therefore, this paper investigates the aging-related degradation of a control rod drive system by integrating Dynamic Object-Oriented Bayesian Network and Hidden Markov Model. Uncertainties in the degradation of the control system and mechanical system are addressed by using fuzzy theory and the Hidden Markov Model respectively. A system which consists of eight control rod drive mechanisms divided into two groups is used to demonstrate the method. The aging-related degradation of the control rod drive system is analyzed by the Bayesian inference algorithm based on the accelerated life test data, and the impact of different operating schemes on the system performance is also investigated. Meanwhile, the components or units that have major impact on the system's performance are identified at different operational phases. Finally, several essential safety measures are suggested to mitigate the risk caused by the system degradation.

Keywords

Acknowledgement

The authors thankfully acknowledge the financial support provided by the key laboratory of nuclear reactor system design open fund (HT-KFKT-02-2017101). Meanwhile the authors would like to thank the associate editor and anonymous reviewers for their constructive suggestions and comments on the previous version of this paper.

References

  1. Quanquan Wang, Yongping Li, Yongbo Wei, Yongzhong Chen, Lifeng Han, Xuejing Sun, TMSR single control rod drive mechanism control system and its reliability analysis, in: 2nd International Conference on Measurement, Information and Control. Harbin, CHINA, 2013.
  2. Syaiful Bakhri Deswandri, Investigation of rod control system reliability of PWR reactors, KnE Energy 2016 (2016) 11, https://doi.org/10.18502/ken.v1i1.465.
  3. E. Ramesh, S. Usha, Reliability analysis of control rod drive mechanisms of FBTR for reactor startup and power control, in: 2010 2nd International Conference on Reliability, Safety and Hazard (ICRESH-2010), dec.2010, pp. 431-435. Mumbai, India.
  4. S.D. Caylor, J.B. McConkey, G.W. Morton, H.M. Hashemian, On-line monitoring and diagnostics for rod control systems in nuclear power plants, in: American Nuclear Society 9th International Topical Meeting on Nuclear Plant Instrumentation, Control and Human-Machine Interface Technologies, Charlotte, NC, 2015. Feb 2015.
  5. Adebena Oluwasegun, Jae-Cheon Jung, The application of machine learning for the prognostics and health management of control element drive system, Nucl. Eng. Technol. 52 (2020) 2262-2273. https://doi.org/10.1016/j.net.2020.03.028
  6. W. Gunther, K. Sullivan, Aging Assessment of the Westinghouse PWR Control Rod Drive System, Upton, NY, 1991, 11973.
  7. Kai Song, Jian Shi, Xiaojian Yi, Yongcheng Xie, Gang liu, Mingchao Lu, Accelerated life data analysis for control rod drive mechanism coil, in: 2019 international conference on sensing, diagnostics, prognostics and control 2, August 2019, pp. 940-943. Beijing China.
  8. D. Hertz, Approach to analysis of wear mechanisms in the case of RCCAs and CRDM latch arms: from observation to understanding, Wear 261 (9) (2006) 1024-1031. https://doi.org/10.1016/j.wear.2006.03.037
  9. Sitong Ling Wenqiang Li Tianda Yu Qiang Deng, Guozhong Fu. Analysis and optimization research on latch life of control rod drive mechanism based on approximate model, Nucl. Eng. Technol., http://doi.org/10.1016/j.net.2021.06.012..
  10. Weber p., Medina-Oliva G., Simon C., Iung B. Overview on Bayesian networks application for dependability, risk analysis and maintenance areas. Eng. Appl. Artif. Intell. doi:10.1016/j.engappai.2010.06.002
  11. M. Haddara, F.I. Khan, L. Krishnasamy, A new methodology for risk-based availability analysis, IEEE Trans. Reliab. 57 (2008) 103-112, https://doi.org/10.1109/TR.2007.911248.
  12. M. Abimbola, F. Khan, N. Khakzad, S. Butt, Safety and risk analysis of managed pressure drilling operation using Bayesian network, Saf. Sci. 76 (2015) 133-144. https://doi.org/10.1016/j.ssci.2015.01.010
  13. Arko Ghosh, Salim Ahmed, Faisal Khan, Modeling and testing of temporal dependency in the failure of a process system, Ind. Eng. Chem. Res. 58 (2019) 8162-8171. https://doi.org/10.1021/acs.iecr.8b06300
  14. MdTanjin Amin, Faisal Khan, Syed Imtiaz. Dynamic Availability Assessment of Safety Critical Systems Using a Dynamic Bayesian Network, Reliability Engineering and System Safety, doi:10.1016/j.ress.2018.05.017.
  15. Mihiran Galagedarage Don, Faisal Khan, Dynamic process fault detection and diagnosis based on a combined approach of hidden Markov and Bayesian network model, Chem. Eng. Sci. 201 (2019) 82-96. https://doi.org/10.1016/j.ces.2019.01.060
  16. Anselm Lorenzoni, Michael Kempf, Mannuss. Oliver, Degradation model constructed with the aid of dynamic Bayesian networks, Cogent Eng. 4 (2017), 1395786.
  17. Yuanjiang Chang, Changshuai Zhang, Guoming Chen, Xiuquan Liu, Jiayi Li, Baoping Cai, Liangbin Xu, Dynamic Bayesian networks based approach for risk analysis of subsea wellhead fatigue failure during service life, Reliab. Eng. Syst. Saf. 188 (2019) 454-462. https://doi.org/10.1016/j.ress.2019.03.040
  18. Pedro A. Perez Ramirez, Ingrid Bouwer Utne, Use of dynamic Bayesian networks for life extension assessment of aging systems, Reliab. Eng. Syst. Saf. 133 (2015) 119-136. https://doi.org/10.1016/j.ress.2014.09.002
  19. Daniel Straub, Stochastic modeling of deterioration processes through dynamic Bayesian networks, J. Eng. Mech. 135 (2009) 1089-1099. https://doi.org/10.1061/(ASCE)EM.1943-7889.0000024
  20. Thanh-Binh Tran, Emilio Bastidas-Arteaga, Younes Aoues, A dynamic Bayesian network for spatial deterioration modelling and reliability updating of timber structures subjected to decay, Eng. Struct. 209 (2020), 110301.
  21. Xiao Feng Liang, Hong Dong Wang, Hong Yi, Dan Li, Warship reliability evaluation based on dynamic Bayesian networks and numerical simulation, Ocean Eng. 136 (2017) 129-140. https://doi.org/10.1016/j.oceaneng.2017.03.023
  22. Philippe Weber, Lionel Jouffe, Complex system reliability modelling with dynamic object oriented Bayesian networks, Reliab. Eng. Syst. Saf. 91 (2006) 149-162. https://doi.org/10.1016/j.ress.2005.03.006
  23. Baoping Cai, Hanlin, Min Xie. A real-time fault diagnosis methodology of complex systems using object-oriented Bayesian networks. Mech. Syst. Signal Process.. https://doi.org/10.1016/j.ymssp.2016.04.019.
  24. G. Weidl, A.L. Madsen, S. Israelson, Applications of object-oriented Bayesian networks for condition monitoring, root cause analysis and decision support on operation of complex continuous processes, Comput. Chem. Eng. 29 (9) (2005) 1996-2009. https://doi.org/10.1016/j.compchemeng.2005.05.005
  25. Sinda Rebello, Hongyang Yu, Ma Lin, An integrated approach for system functional reliability assessment using Dynamic Bayesian network and hidden markov model, Reliab. Eng. Syst. Saf. 180 (2018) 124-135. https://doi.org/10.1016/j.ress.2018.07.002
  26. K.P. Murphy, Dynamic Bayesian Networks: Representation, Inference and Learning, PhD Thesis, 2002, pp. 1-281, 10.1.1.129.7714. https://doi.org/10.1.1.129.7714
  27. X. Wu, H. Liu, L. Zhang, M.J. Skibniewski, Q. Deng, J. Teng, A dynamic Bayesian net-work based approach to safety decision support in tunnel construction, Reliab. Eng. Syst. Saf. 134 (2015) 157-168. https://doi.org/10.1016/j.ress.2014.10.021
  28. Feng Wang, Qiuping Shen, Aging mechanism and effect analysis of control rod drive mechanism, Mech. Eng. 6 (2014) 74-79.
  29. Xinhong Li, Guoming Chen, Faisal Khan, Changhang Xu, Dynamic risk assessment of subsea pipelines leaking using precursor data, Ocean Eng. 178 (2019) 156-169. https://doi.org/10.1016/j.oceaneng.2019.02.009
  30. Seyed Miri Lavasani, Nahid Ramzali, Farinaz Sabzalipour, Emre Akyuz, Utilisation of Fuzzy Fault Tree Analysis (FFTA) for quantified risk analysis of leakage in abandoned oil and natural-gas wells, Ocean Eng. 108 (2015) 729-737. https://doi.org/10.1016/j.oceaneng.2015.09.008
  31. A. Mentes, I. Helvacioglu, An application of fuzzy fault tree analysis for spread mooring systems, Ocean Eng. 38 (2011) 285-294. https://doi.org/10.1016/j.oceaneng.2010.11.003
  32. L. Shi, J. Shuai, K. Xu, Fuzzy fault tree assessment based on improved AHP for fire and explosion accidents for steel oil storage tanks, J. Hazard Mater. 278 (2014) 529-538. https://doi.org/10.1016/j.jhazmat.2014.06.034
  33. Guizhen Zhang, V. Vinh, Thai. Expert elicitation and Bayesian network modeling for shipping accidents: a literature review, Saf. Sci. 87 (2016) 53-62. https://doi.org/10.1016/j.ssci.2016.03.019
  34. W. Li, P. Poupart, P.V. Beek, Exploiting structure in weighted model counting approaches to probabilistic inference, Artif. Intell. Res. 40 (2011) 729-765. https://doi.org/10.1613/jair.3232
  35. L.A. Zadeh, Fuzzy sets, Inf. Control. 8 (8) (1965) 338-353. https://doi.org/10.1016/S0019-9958(65)90241-X
  36. Math works Inc, MATLAB software, Natick, MA, USA. https://www.mathworks.com/.
  37. D. Chelidze, J.P. Cusumano, Phase space warping: nonlinear time-series analysis for slowly drifting systems, Phil. Transac. Math. Phys. Eng. Sci. 364 (1846) (2006) 2495-2513.
  38. GeNIe, Decision Systems Laboratory, 1998, 2015. Available at: https://dslpitt.org/genie/.