Nuclear Engineering and Technology

Korean Nuclear Society (한국원자력학회)
권호 표지
DOI QR코드

DOI QR Code

Dependence assessment in human reliability analysis under uncertain and dynamic situations

  • Gao, Xianghao (School of Automation Engineering, Shanghai University of Electric Power) ;
  • Su, Xiaoyan (School of Automation Engineering, Shanghai University of Electric Power) ;
  • Qian, Hong (School of Automation Engineering, Shanghai University of Electric Power) ;
  • Pan, Xiaolei (School of Automation Engineering, Shanghai University of Electric Power)
  • Received : 2021.06.18
  • Accepted : 2021.09.10
  • Published : 2022.03.25
Download PDF
⟨ Previous Next ⟩

Abstract

Since reliability and security of man-machine system increasingly depend on reliability of human, human reliability analysis (HRA) has attracted a lot of attention in many fields especially in nuclear engineering. Dependence assessment among human tasks is a important part in HRA which contributes to an appropriate evaluation result. Most of methods in HRA are based on experts' opinions which are subjective and uncertain. Also, the dependence influencing factors are usually considered to be constant, which is unrealistic. In this paper, a new model based on Dempster-Shafer evidence theory (DSET) and fuzzy number is proposed to handle the dependence between two tasks in HRA under uncertain and dynamic situations. First, the dependence influencing factors are identified and the judgments on the factors are represented as basic belief assignments (BBAs). Second, the BBAs of the factors that varying with time are reconstructed based on the correction BBA derived from time value. Then, BBAs of all factors are combined to gain the fused BBA. Finally, conditional human error probability (CHEP) is derived based on the fused BBA. The proposed method can deal with uncertainties in the judgments and dynamics of the dependence influencing factors. A case study is illustrated to show the effectiveness and the flexibility of the proposed method.

Keywords

Acknowledgement

The work is partially supported by Shanghai Natural Science Foundation (Grant No.19ZR1420700), sponsored by Shanghai Rising-Star Program (Grant No. 21QA1403400), Shanghai Key Laboratory of Power Station Automation Technology (Grant No. 13DZ2273800).

References

  1. D. Kancev, A plant-specific HRA sensitivity analysis considering dynamic operator actions and accident management actions, Nuclear Engineering and Technology 52 (9) (2020) 1983-1989. https://doi.org/10.1016/j.net.2020.02.021
  2. Y. Cai, M.W. Golay, A framework analyzing system status and human activities: illustrated using 2011 Fukushima nuclear power plant accident scenarios, Nucl. Eng. Des. 373 (3) (2021) 111025. https://doi.org/10.1016/j.nucengdes.2020.111025
  3. A. Zhang, F. Gao, M. Yang, W. Bi, Belief rule-based dependence assessment method under interval uncertainty, Quality and Reliability Engineering 36 (5) (2020) 2459-2477. https://doi.org/10.1002/qre.2708
  4. A. Abbaspour, M. Saremi, A. Alibabaei, P.S. Moghanlu, Determining the optimal human reliability analysis (HRA) method in healthcare systems using Fuzzy ANP and Fuzzy TOPSIS, Journal of Patient Safety and Risk Management 25 (3) (2020) 123-133. https://doi.org/10.1177/2516043519900431
  5. K. Groth, R. Smith, R. Moradi, A hybrid algorithm for developing third generation HRA methods using simulator data, causal models, and cognitive science, Reliab. Eng. Syst. Saf. 191 (2019) 106507. https://doi.org/10.1016/j.ress.2019.106507
  6. S. Abrishami, N. Khakzad, S.M. Hosseini, A data-based comparison of BN-HRA models in assessing human error probability: an offshore evacuation case study, Reliab. Eng. Syst. Saf. 202 (2020) 107043. https://doi.org/10.1016/j.ress.2020.107043
  7. M. Tavakoli, M. Nafar, Human reliability analysis in maintenance team of power transmission system protection, Protection and Control of Modern Power Systems 5 (4) (2020) 270-282.
  8. E. Zio, P. Baraldi, M. Librizzi, L. Podofillini, V. Dang, A fuzzy set-based approach for modeling dependence among human errors, Fuzzy Set Syst. 160 (13) (2009) 1947-1964. https://doi.org/10.1016/j.fss.2009.01.016
  9. X. Zhou, X. Deng, Y. Deng, S. Mahadevan, Dependence assessment in human reliability analysis based on D numbers and AHP, Nucl. Eng. Des. 313 (2017) 243-252. https://doi.org/10.1016/j.nucengdes.2016.12.001
  10. A. Swain, H. Guttmann, Handbook of Human Reliability Analysis with Emphasis on Nuclear Power Plant Applications. NUREG/CR-1278, US Nuclear Regulatory Commission, Washington, DC, 1983.
  11. L. Podofillini, V. Dang, E. Zio, P. Baraldi, M. Librizzi, Using expert models in human reliability analysis-a dependence assessment method based on fuzzy logic, Risk Anal. Int. J. 30 (8) (2010) 1277-1297. https://doi.org/10.1111/j.1539-6924.2010.01425.x
  12. D. Gertman, H. Blackman, J. Marble, J. Byers, C. Smith, The SPAR-H Human Reliability Analysis Method, US Nuclear Regulatory Commission, 2005.
  13. M. Cepin, Importance of human contribution within the human reliability analysis (IJS-HRA), J. Loss Prev. Process. Ind. 21 (3) (2008) 268-276. https://doi.org/10.1016/j.jlp.2007.04.012
  14. A. Prosek, M. Cepin, Success criteria time windows of operator actions using RELAP5/MOD3.3 within human reliability analysis, J. Loss Prev. Process. Ind. 21 (3) (2008) 260-267. https://doi.org/10.1016/j.jlp.2007.06.010
  15. Y. Kirimoto, Y. Hirotsu, K. Nonose, Development of a human reliability analysis (HRA) guide for qualitative analysis with emphasis on narratives and models for tasks in extreme conditions, Nuclear Engineering and Technology 53 (2) (2021) 376-385. https://doi.org/10.1016/j.net.2020.10.004
  16. M. Cepin, DEPEND-HRA-A method for consideration of dependency in human reliability analysis, Reliab. Eng. Syst. Saf. 93 (10) (2008) 1452-1460. https://doi.org/10.1016/j.ress.2007.10.004
  17. X. Guo, Y. Zhou, J. Qian, Y. Deng, Using evidence credibility decay model for dependence assessment in human reliability analysis, Ann. Nucl. Energy 100 (2017) 107-118. https://doi.org/10.1016/j.anucene.2016.10.007
  18. L. Chen, X. Zhou, F. Xiao, Y. Deng, S. Mahadevan, Evidential analytic hierarchy process dependence assessment methodology in human reliability analysis, Nuclear Engineering and Technology 49 (1) (2017) 123-133. https://doi.org/10.1016/j.net.2016.10.003
  19. P. Dempster, Upper and lower probabilities induced by a multivalued mapping, Ann. Math. Stat. 38 (2) (1967) 325-339. https://doi.org/10.1214/aoms/1177698950
  20. G. Shafer, in: A Mathematical Theory of Evidence, vol. 42, Princeton university press, 1976.
  21. Y. Deng, Uncertainty measure in evidence theory, Sci. China Inf. Sci. 63 (11) (2020) 210201. https://doi.org/10.1007/s11432-020-3006-9
  22. Z. Liu, Y. Liu, J. Dezert, F. Cuzzolin, Evidence combination based on credal belief redistribution for pattern classification, IEEE Trans. Fuzzy Syst. 28 (4) (2020) 618-631. https://doi.org/10.1109/tfuzz.2019.2911915
  23. Z. Liu, L. Huang, K. Zhou, T. Denoeux, Combination of transferable classification with multisource domain adaptation based on evidential reasoning, IEEE Trans. Neural Networks and Learning Systems 32 (5) (2021), 2015-2029. https://doi.org/10.1109/TNNLS.2020.2995862
  24. D. Vo, J. Karnjana, V. Huynh, An integrated framework of learning and evidential reasoning for user profiling using short texts, Inf. Fusion 70 (2021) 27-42. https://doi.org/10.1016/j.inffus.2020.12.004
  25. Y. Du, Q. Chen, Y. Sun, C. Li, Knowledge structure-based consensus-reaching method for large-scale multiattribute group decision-making, Knowl. Base Syst. 219 (2021) 106885. https://doi.org/10.1016/j.knosys.2021.106885
  26. Y. Pan, L. Zhang, X. Wu, M.J. Skibniewski, Multi-classifier information fusion in risk analysis, Inf. Fusion 60 (2020) 121-136. https://doi.org/10.1016/j.inffus.2020.02.003
  27. Y. Pan, L. Zhang, Z. Li, L. Ding, Improved fuzzy bayesian network-based risk analysis with interval-valued fuzzy sets and D-S evidence theory, IEEE Trans. Fuzzy Syst. 28 (9) (2019) 2063-2077. https://doi.org/10.1109/tfuzz.2019.2929024
  28. J. Zhao, Y. Deng, Performer selection in human reliability analysis: D numbers approach, Int. J. Comput. Commun. Control 14 (3) (2019) 437-452. https://doi.org/10.15837/ijccc.2019.3.3537
  29. Z. Liu, L. Huang, K. Zhou, T. Denoeux, Combination of classifiers with different frames of discernment based on belief functions, IEEE Trans. Fuzzy Syst. (2020) 2995862.
  30. Y. Deng, Information volume of mass function, Int. J. Comput. Commun. Control 15 (6) (2020) 3983. https://doi.org/10.15837/ijccc.2020.6.3983
  31. J. Deng, Y. Deng, Information volume of fuzzy membership function, Int. J. Comput. Commun. Control 16 (1) (2021) 4106.
  32. Y. Xue, Y. Deng, A new belief structure based on cardinality measure, Comput. Appl. Math. 40 (2) (2021) 59. https://doi.org/10.1007/s40314-021-01452-3
  33. X. Deng, W. Jiang, Dependence assessment in human reliability analysis using an evidential network approach extended by belief rules and uncertainty measures, Ann. Nucl. Energy 117 (2018) 183-193. https://doi.org/10.1016/j.anucene.2018.03.028
  34. W. Jiang, Y. Cao, X. Deng, A novel Z-network model based on bayesian network and Z-number, IEEE Trans. Fuzzy Syst. (2019) 1585-1599.
  35. X. Zheng, Y. Deng, Dependence assessment in human reliability analysis based on evidence credibility decay model and Iowa operator, Ann. Nucl. Energy 112 (2018) 673-684. https://doi.org/10.1016/j.anucene.2017.10.045
  36. A. Jousselme, D. Grenier, Eloi Bosse, A new distance between two bodies of evidence, Inf. Fusion 2 (2) (2001) 91-101. https://doi.org/10.1016/S1566-2535(01)00026-4
  37. Y. Gong, X. Su, H. Qian, N. Yang, Research on fault diagnosis methods for the reactor coolant system of nuclear power plant based on D-S evidence theory, Ann. Nucl. Energy 112 (2018) 395-399. https://doi.org/10.1016/j.anucene.2017.10.026
  38. L. Zadeh, Fuzzy sets, Inf. Control 8 (3) (1965) 338-353. https://doi.org/10.1016/S0019-9958(65)90241-X
  39. F. Xiao, Evidential fuzzy multicriteria decision making based on belief entropy, IEEE Trans. Fuzzy Syst. 28 (7) (2020) 1477-1491. https://doi.org/10.1109/tfuzz.2019.2936368
  40. Y. Deng, W. Shi, Z. Zhu, Q. Liu, Combining belief functions based on distance of evidence, Decis. Support Syst. 38 (2004) 489-493. https://doi.org/10.1016/j.dss.2004.04.015