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http://dx.doi.org/10.1016/j.net.2022.06.020

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)
Publication Information
Nuclear Engineering and Technology / v.54, no.11, 2022 , pp. 4111-4124 More about this Journal
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
Control rod drive system(CRDS); Aging-related degradation; Dynamic object oriented bayesian; network(DOOBN); Hidden markov model(HMM); Fuzzy theory; Control rod drive mechanism(CRDM);
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1 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.   DOI
2 Daniel Straub, Stochastic modeling of deterioration processes through dynamic Bayesian networks, J. Eng. Mech. 135 (2009) 1089-1099.   DOI
3 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.
4 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.   DOI
5 Philippe Weber, Lionel Jouffe, Complex system reliability modelling with dynamic object oriented Bayesian networks, Reliab. Eng. Syst. Saf. 91 (2006) 149-162.   DOI
6 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.   DOI
7 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.   DOI
8 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.   DOI
9 K.P. Murphy, Dynamic Bayesian Networks: Representation, Inference and Learning, PhD Thesis, 2002, pp. 1-281, 10.1.1.129.7714.   DOI
10 Feng Wang, Qiuping Shen, Aging mechanism and effect analysis of control rod drive mechanism, Mech. Eng. 6 (2014) 74-79.
11 Xinhong Li, Guoming Chen, Faisal Khan, Changhang Xu, Dynamic risk assessment of subsea pipelines leaking using precursor data, Ocean Eng. 178 (2019) 156-169.   DOI
12 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.   DOI
13 A. Mentes, I. Helvacioglu, An application of fuzzy fault tree analysis for spread mooring systems, Ocean Eng. 38 (2011) 285-294.   DOI
14 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.   DOI
15 Guizhen Zhang, V. Vinh, Thai. Expert elicitation and Bayesian network modeling for shipping accidents: a literature review, Saf. Sci. 87 (2016) 53-62.   DOI
16 W. Li, P. Poupart, P.V. Beek, Exploiting structure in weighted model counting approaches to probabilistic inference, Artif. Intell. Res. 40 (2011) 729-765.   DOI
17 L.A. Zadeh, Fuzzy sets, Inf. Control. 8 (8) (1965) 338-353.   DOI
18 Math works Inc, MATLAB software, Natick, MA, USA. https://www.mathworks.com/.
19 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.
20 GeNIe, Decision Systems Laboratory, 1998, 2015. Available at: https://dslpitt.org/genie/.
21 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.   DOI
22 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.   DOI
23 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.
24 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.   DOI
25 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.
26 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.
27 W. Gunther, K. Sullivan, Aging Assessment of the Westinghouse PWR Control Rod Drive System, Upton, NY, 1991, 11973.
28 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.
29 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.   DOI
30 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..   DOI
31 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   DOI
32 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.   DOI
33 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.   DOI
34 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.   DOI
35 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.   DOI
36 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.   DOI
37 Anselm Lorenzoni, Michael Kempf, Mannuss. Oliver, Degradation model constructed with the aid of dynamic Bayesian networks, Cogent Eng. 4 (2017), 1395786.
38 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.   DOI