Nuclear Engineering and Technology

  • 제49권1호
  • /
  • Pages.113-123
  • /
  • 2017
  • /
  • 1738-5733(pISSN)
  • /
  • 2234-358X(eISSN)
한국원자력학회 (Korean Nuclear Society)
권호 표지
DOI QR코드

DOI QR Code

Evidential Analytic Hierarchy Process Dependence Assessment Methodology in Human Reliability Analysis

  • Chen, Luyuan (School of Computer and Information Science, Southwest University) ;
  • Zhou, Xinyi (School of Computer and Information Science, Southwest University) ;
  • Xiao, Fuyuan (School of Computer and Information Science, Southwest University) ;
  • Deng, Yong (School of Computer and Information Science, Southwest University) ;
  • Mahadevan, Sankaran (School of Engineering, Vanderbilt University)
  • 투고 : 2015.11.20
  • 심사 : 2016.10.21
  • 발행 : 2017.02.25
PDF 다운로드
⟨ 이전 논문 다음 논문 ⟩

초록

In human reliability analysis, dependence assessment is an important issue in risky large complex systems, such as operation of a nuclear power plant. Many existing methods depend on an expert's judgment, which contributes to the subjectivity and restrictions of results. Recently, a computational method, based on the Dempster-Shafer evidence theory and analytic hierarchy process, has been proposed to handle the dependence in human reliability analysis. The model can deal with uncertainty in an analyst's judgment and reduce the subjectivity in the evaluation process. However, the computation is heavy and complicated to some degree. The most important issue is that the existing method is in a positive aspect, which may cause an underestimation of the risk. In this study, a new evidential analytic hierarchy process dependence assessment methodology, based on the improvement of existing methods, has been proposed, which is expected to be easier and more effective.

키워드

참고문헌

  1. E. Zio, Risk-informed regulation: handling uncertainty for a rational management of safety, Nucl. Eng. Technol. 40 (2008) 327-348. https://doi.org/10.5516/NET.2008.40.5.327
  2. E. Pate-Cornell, On black swans and perfect storms: risk analysis and management when statistics are not enough, Risk Anal. 32 (2012) 1823-1833. https://doi.org/10.1111/j.1539-6924.2011.01787.x
  3. M.R. Martins, M.C. Maturana, Human error contribution in collision and grounding of oil tankers, Risk Anal. 30 (2010) 674-698. https://doi.org/10.1111/j.1539-6924.2010.01392.x
  4. M. Cepin, DEPEND-HRA-a method for consideration of dependency inhuman reliability analysis, Reliab. Eng. Syst. Saf. 93 (2008) 1452-1460. https://doi.org/10.1016/j.ress.2007.10.004
  5. R.L. Brune, M. Weinstein, M. Fitzwater, Peer-review Study of the Draft Handbook for Human-reliability Analysis with Emphasis on Nuclear-power-plant Applications, NUREG/CR-1278, Technical Report, Human Performance Technologies, Inc., Thousand Oaks (CA), 1983.
  6. E. Zio, P. Baraldi, M. Librizzi, L. Podofillini, V.N. Dang, A fuzzy set-based approach for modeling dependence among human errors, Fuzzy Sets Syst. 160 (2009) 1947-1964. https://doi.org/10.1016/j.fss.2009.01.016
  7. B. Reer, Review of advances in human reliability analysis of errors of commission, part 1: EOC identification, Reliab. Eng. Syst. Saf. 93 (2008) 1091-1104. https://doi.org/10.1016/j.ress.2007.07.005
  8. M. Marseguerra, E. Zio, M. Librizzi, Human reliability analysis by fuzzy cream, Risk Anal. 27 (2007) 137-154. https://doi.org/10.1111/j.1539-6924.2006.00865.x
  9. L. Podofillini, J. Park, V.N. Dang, Measuring the influence of task complexity on human error probability: an empirical evaluation, Nucl. Eng. Technol. 45 (2013) 151-164. https://doi.org/10.5516/NET.04.2013.702
  10. W.D. Jung, A.M. Whaley, B.P. Hallbert, Human errors during the simulations of an SGTR scenario: application of the HERA system, Nucl. Eng. Technol. 41 (2009) 1361-1374. https://doi.org/10.5516/NET.2009.41.10.1361
  11. A. Swain, THERP-technique for human error rate prediction, Proceedings of the Symposium on Quantification of Human Performance, Albuquerque, 1964.
  12. B. Kirwan, The validation of three human reliability quantification techniques-THERP, HEART and JHEDI: Part 1-technique descriptions and validation issues, Appl. Ergon. 27 (1996) 359-373. https://doi.org/10.1016/S0003-6870(96)00044-0
  13. J. Grobbelaar, J. Julius, F. Rahn, et al., Analysis of dependent human failure events using the EPRI HRA calculator, in: Proceedings of the ANS Topical Meeting on Probabilistic Safety Assessment (PSA'05), 2005, pp. 11-15.
  14. 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. 30 (2010) 1277-1297. https://doi.org/10.1111/j.1539-6924.2010.01425.x
  15. X. Su, S. Mahadevan, P. Xu, Y. Deng, Dependence assessment in human reliability analysis using evidence theory and AHP, Risk Anal. 35 (2015) 1296-1316. https://doi.org/10.1111/risa.12347
  16. C.K. Lo, N. Pedroni, E. Zio, Treating uncertainties in a nuclear seismic probabilistic risk assessment by means of the Dempster-Shafer theory of evidence, Nucl. Eng. Technol. 46 (2014) 11-26. https://doi.org/10.5516/NET.03.2014.701
  17. X. Ning, J. Yuan, X. Yue, Uncertainty-based optimization algorithms in designing fractionated spacecraft, Sci. Rep. 6 (2016) 22979. https://doi.org/10.1038/srep22979
  18. W. Du, Y. Gao, C. Liu, Z. Zheng, Z. Wang, Adequate is better: particle swarm optimization with limited-information, Appl. Math. Comput. 268 (2015) 832-838.
  19. W. Jiang, B.Wei, J. Zhan, C. Xie, D. Zhou, A visibility graph power averaging aggregation operator: a methodology based on network analysis, Comput. Ind. Eng. 101 (2016) 260-268. https://doi.org/10.1016/j.cie.2016.09.009
  20. L. Wu, Y.M. Zhuang, W. Li, A new default intensity model with fuzziness and hesitation, Int. J. Comput. Intell. Syst. 9 (2016) 340-350. https://doi.org/10.1080/18756891.2016.1161345
  21. W. Jiang, C. Xie, M. Zhuang, Y. Shou, Y. Tang, Sensor data fusion with Z-numbers and its application in fault diagnosis, Sensors 16 (2016) 1-22. https://doi.org/10.1109/JSEN.2016.2616227
  22. X. Ning, J. Yuan, X. Yue, A. Ramirez-Serrano, Induced generalized choquet aggregating operators with linguistic information and their application to multiple attribute decision making based on the intelligent computing, J. Intell. Fuzzy Syst. 27 (2014) 1077-1085.
  23. F. Sabahi, M.-R. Akbarzadeh-T, A qualified description of extended fuzzy logic, Inf. Sci. 244 (2013) 60-74. https://doi.org/10.1016/j.ins.2013.03.020
  24. F. Sabahi, M.-R. Akbarzadeh-T, Introducing validity in fuzzy probability for judicial decision-making, Int. J. Approx. Reason 55 (2014) 1383-1403. https://doi.org/10.1016/j.ijar.2013.12.003
  25. T. Denoeux, Conjunctive and disjunctive combination of belief functions induced by nondistinct bodies of evidence, Artif. Intell. 172 (2008) 234-264. https://doi.org/10.1016/j.artint.2007.05.008
  26. L.A. Zadeh, A simple view of the Dempster-Shafer theory of evidence and its implication for the rule of combination, AI Mag 7 (1986) 85.
  27. Y. Deng, Deng entropy, Chaos Soliton. Fract. 91 (2016) 549-553. https://doi.org/10.1016/j.chaos.2016.07.014
  28. E. Lefevre, O. Colot, P. Vannoorenberghe, Belief function combination and conflict management, Inf. Fusion 3 (2002) 149-162. https://doi.org/10.1016/S1566-2535(02)00053-2
  29. J. Ma, W. Liu, P. Miller, H. Zhou, An evidential fusion approach for gender profiling, Inf. Sci. 333 (2016) 10-20. https://doi.org/10.1016/j.ins.2015.11.011
  30. C. Fu, J.B. Yang, S.L. Yang, A group evidential reasoning approach based on expert reliability, Eur. J. Oper. Res. 246 (2015) 886-893. https://doi.org/10.1016/j.ejor.2015.05.042
  31. Y. Deng, S. Mahadevan, D. Zhou, Vulnerability assessment of physical protection systems: a bio-inspired approach, Int. J. Unconv. Comput. 11 (2015) 227-243.
  32. Y. Song, X. Wang, L. Lei, S. Yue, Uncertainty measure for interval-valued belief structures, Measurement 80 (2016) 241-250. https://doi.org/10.1016/j.measurement.2015.11.032
  33. Y. Yang, D. Han, A new distance-based total uncertainty measure in the theory of belief functions, Knowledge Based Syst. 94 (2016) 114-123. https://doi.org/10.1016/j.knosys.2015.11.014
  34. H.C. Liu, J.X. You, X.J. Fan, Q.L. Lin, Failure mode and effects analysis using D numbers and grey relational projection method, Expert Syst. Appl. 41 (2014) 4670-4679. https://doi.org/10.1016/j.eswa.2014.01.031
  35. J. Guo, A risk assessment approach for failure mode and effects analysis based on intuitionistic fuzzy sets and evidence theory, J. Intell. Fuzzy Syst. 30 (2016) 869-881. https://doi.org/10.3233/IFS-151809
  36. W. Jiang, C. Xie, B. Wei, D. Zhou, A modified method for risk evaluation in failure modes and effects analysis of aircraft turbine rotor blades, Adv. Mech. Eng. 8 (2016) 1-16.
  37. Y. Li, J. Chen, F. Ye, D. Liu, The improvement of DS evidence theory and its application in IR/MMW target recognition, J. Sensors 2016 (2016) 1903792.
  38. Z. Liu, Q. Pan, J. Dezert, G. Mercier, Credal c-means clustering method based on belief functions, Knowledge Based Syst. 74 (2015) 119-132. https://doi.org/10.1016/j.knosys.2014.11.013
  39. W. Jiang, B. Wei, C. Xie, D. Zhou, An evidential sensor fusion method in fault diagnosis, Adv. Mech. Eng. 8 (2016) 1-7.
  40. Y. Deng, Generalized evidence theory, Appl. Intell. 43 (2015) 530-543. https://doi.org/10.1007/s10489-015-0661-2
  41. W. Jiang, B. Wei, X. Qin, J. Zhan, Y. Tang, Sensor data fusion based on a new conflict measure, Math. Probl. Eng. 2016 (2016) Article ID: 5769061.
  42. X. Su, S. Mahadevan, W. Han, Y. Deng, Combining dependent bodies of evidence, Appl. Intell. 44 (2016) 634-644. https://doi.org/10.1007/s10489-015-0723-5
  43. W. Jiang, J. Zhan, D. Zhou, X. Li, A method to determine generalized basic probability assignment in the open world, Math. Probl. Eng. 2016 (2016) Article ID: 3878634.
  44. W. Liu, Analyzing the degree of conflict among belief functions, Artif. Intell. 170 (2006) 909-924. https://doi.org/10.1016/j.artint.2006.05.002
  45. T.L. Saaty. The Analytic Hierarchy Process: Planning, Priority Setting, Resources Allocation, University of Pittsburgh, Pittsburgh PA, 1980.
  46. X. Deng, Y. Hu, Y. Deng, S. Mahadevan, Supplier selection using AHP methodology extended by D numbers, Expert Syst. Appl. 41 (2014) 156-167. https://doi.org/10.1016/j.eswa.2013.07.018
  47. R.K. Goyal, S. Kaushal, A constrained non-linear optimization model for fuzzy pairwise comparison matrices using teaching learning based optimization, Appl. Intell. 45 (2016) 652-661. https://doi.org/10.1007/s10489-016-0777-z
  48. X. Ning, T. Zhang, Y. Wu, P. Zhang, J. Zhang, S. Li, X. Yue, J. Yuan, Coordinated parameter identification technique for the inertial parameters of non-cooperative target, PLoS One 11 (2016) e0153604. https://doi.org/10.1371/journal.pone.0153604
  49. D. Cheng, R.X. Hao, Y.Q. Feng, Embedding even cycles on folded hypercubes with conditional faulty edges, Inf. Process. Lett. 115 (2015) 945-949. https://doi.org/10.1016/j.ipl.2015.07.015
  50. W.B. Du, X.L. Zhou, O. Lordan, Z.Wang, C. Zhao, Y.B. Zhu, Analysis of the Chinese airline network as multi-layer networks, Transport. Res. E Log. Transport. Rev. 89 (2016) 108-116. https://doi.org/10.1016/j.tre.2016.03.009
  51. Z. Liu, Q. Pan, J. Dezert, A. Martin, Adaptive imputation of missing values for incomplete pattern classification, Pattern Recogn. 52 (2016) 85-95. https://doi.org/10.1016/j.patcog.2015.10.001
  52. A. Mardani, A. Jusoh, E.K. Zavadskas, Fuzzy multiple criteria decision-making techniques and applications-two decades review from 1994 to 2014, Expert Syst. Appl. 42 (2015) 4126-4148. https://doi.org/10.1016/j.eswa.2015.01.003
  53. W. Jiang, Y. Luo, X. Qin, J. Zhan, An improved method to rank generalized fuzzy numbers with different left heights and right heights, J. Intell. Fuzzy Syst. 28 (2015) 2343-2355. https://doi.org/10.3233/IFS-151639
  54. G. Fan, D. Zhong, F. Yan, P. Yue, A hybrid fuzzy evaluation method for curtain grouting efficiency assessment based on an AHP method extended by D numbers, Expert Syst. Appl. 44 (2016) 289-303. https://doi.org/10.1016/j.eswa.2015.09.006
  55. H.T. Nguyen, S.Z.M. Dawal, Y. Nukman, H. Aoyama, K. Case, An integrated approach of fuzzy linguistic preference based AHP and fuzzy COPRAS for machine tool evaluation, PLoS one 10 (2015) e0133599. https://doi.org/10.1371/journal.pone.0133599
  56. Y. Deng, Fuzzy analytical hierarchy process based on canonical representation on fuzzy numbers, J. Comput. Anal. Appl. 22 (2017) 201-228.
  57. C.K. Murphy, Combining belief functions when evidence conflicts, Decis. Support Syst. 29 (2000) 1-9. https://doi.org/10.1016/S0167-9236(99)00084-6
  58. D. Yong, S. WenKang, Z. ZhenFu, L. Qi, 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

피인용 문헌

  1. A Method to Identify the Incomplete Framework of Discernment in Evidence Theory vol.2017, pp.None, 2017, https://doi.org/10.1155/2017/7635972
  2. Human reliability analysis for operators in the digital main control rooms of nuclear power plants vol.57, pp.6, 2017, https://doi.org/10.1080/00223131.2020.1720848
  3. Research on the Cognitive Reliability Model of DCS+SOP in the Ling’ao Phase II Nuclear Power Plant’s Main Control Room vol.207, pp.1, 2017, https://doi.org/10.1080/00295450.2020.1733376
  4. Group dynamics analysis and the correction of coal miners' unsafe behaviors vol.76, pp.4, 2021, https://doi.org/10.1080/19338244.2020.1795610
  5. A decision theory overview and case-based discussion vol.13, pp.8, 2017, https://doi.org/10.12968/jpar.2021.13.8.320