DOI QR코드

DOI QR Code

Inter-relationships between performance shaping factors for human reliability analysis of nuclear power plants

  • Park, Jooyoung (Department of Nuclear Engineering, Chosun University) ;
  • Jung, Wondea (Integrated Safety Assessment Team, KAERI) ;
  • Kim, Jonghyun (Department of Nuclear Engineering, Chosun University)
  • Received : 2019.01.27
  • Accepted : 2019.07.04
  • Published : 2020.01.25

Abstract

Performance shaping factors (PSFs) in a human reliability analysis (HRA) are one that may influence human performance in a task. Most currently applicable HRA methods for nuclear power plants (NPPs) use PSFs to highlight human error contributors and to adjust basic human error probabilities (HEPs) that assume nominal conditions of NPPs. Thus far, the effects of PSFs have been treated independently. However, many studies in the fields of psychology and human factors revealed that there may be relationships between PSFs. Therefore, the inter-relationships between PSFs need to be studied to better reflect their effects on operator errors. This study investigates these inter-relationships using two data sources and also suggests a context-based approach to treat the inter-relationships between PSFs. Correlation and factor analyses are performed to investigate the relationship between PSFs. The data sources are event reports of unexpected reactor trips in Korea and an experiment conducted in a simulator featuring a digital control room. Thereafter, context-based approaches based on the result of factor analysis are suggested and the feasibility of the grouped PSFs being treated as a new factor to estimate HEPs is examined using the experimental data.

Keywords

References

  1. A.D. Swain, H.E. Guttmann, Handbook of Human-Reliability Analysis with Emphasis on Nuclear Power Plant Applications, Sandia National Labs., Albuquerque, NM (USA), 1983. Final report.
  2. A.M. Arigi, et al., Human and organizational factors for multi-unit probabilistic safety assessment: identification and characterization for the Korean case, Nucl. Eng. Technol. 51 (1) (2019) 104-115. https://doi.org/10.1016/j.net.2018.08.022
  3. U.S.NRC, Technical basis and implementation guidelines for a technique for human event analysis (ATHEANA), in: NUREG-1624, Rev, 2000.
  4. W.D. Jung, D.I. Kang, J.W. Kim, Development of a Standard Method for Human Reliability Analysis of Nuclear Power Plants, Korea Atomic Energy Research Institute, 2005.
  5. F. Sun, S. Zhong, Z. Wu, A method and application study on holistic decision tree for human reliability analysis in nuclear power plant, Chin. J. Nucl. Sci. Eng. 28 (3) (2008) 268-272. https://doi.org/10.3321/j.issn:0258-0918.2008.03.013
  6. D.D. Woods, H.E. Pople Jr., E.M. Roth, Cognitive environment simulation: a tool for modeling intention formation for human reliability analysis, Nucl. Eng. Des. 134 (2-3) (1992) 371-380. https://doi.org/10.1016/0029-5493(92)90153-M
  7. J. Williams, A data-based method for assessing and reducing human error to improve operational performance, in: Human Factors and Power Plants, 1988., Conference Record for 1988 IEEE Fourth Conference on, IEEE, 1988.
  8. E. Hollnagel, Cognitive Reliability and Error Analysis Method (CREAM), Elsevier, 1998.
  9. J. Park, W. Jung, A study on the validity of a task complexity measure for emergency operating procedures of nuclear power plants-comparing with a subjective workload, IEEE Trans. Nucl. Sci. 53 (5) (2006) 2962-2970. https://doi.org/10.1109/TNS.2006.882149
  10. C.J. Patten, et al., Driver experience and cognitive workload in different traffic environments, Accid. Anal. Prev. 38 (5) (2006) 887-894. https://doi.org/10.1016/j.aap.2006.02.014
  11. S.T. Godley, Perceived Pilot Workload and Perceived Safety of RNAV (GNSS) Approaches, 2006.
  12. J. Park, et al., An experimental investigation on relationship between PSFs and operator performances in the digital main control room, Ann. Nucl. Energy 101 (2017) 58-68. https://doi.org/10.1016/j.anucene.2016.10.020
  13. D. Gertman, et al., The SPAR-H Human Reliability Analysis Method, US Nuclear Regulatory Commission, 2005.
  14. Y. Chang, A. Mosleh, Cognitive modeling and dynamic probabilistic simulation of operating crew response to complex system accidents. Part 2: IDAC performance influencing factors model, Reliab. Eng. Syst. Saf. 92 (8) (2007) 1014-1040. https://doi.org/10.1016/j.ress.2006.05.010
  15. Boring, R.L., How Many Performance Shaping Factors Are Necessary for Human Reliability Analysis? 2010, Idaho National Laboratory (INL).
  16. K.M. Groth, A Data-Informed Model of Performance Shaping Factors for Use in Human Reliability Analysis, University of Maryland, College Park, 2009.
  17. M. De Ambroggi, P. Trucco, Modelling and assessment of dependent performance shaping factors through Analytic Network Process, Reliab. Eng. Syst. Saf. 96 (7) (2011) 849-860. https://doi.org/10.1016/j.ress.2011.03.004
  18. W. Jung, A Standard HRA Method for PSA in Nuclear Power Plant, K-HRA method., 2005. KAERI/TR-2961/2005.
  19. D. Embrey, SLIM-MAUD (Success Likelihood Index Methodology Multi-Attribute Utility Decomposition): an Approach to Assessing Human Error Probabilities Using Structured Expert Judgement vol. 2, NUREG/CR3518, 1984.
  20. K.M. Groth, L.P. Swiler, Bridging the gap between HRA research and HRA practice: a Bayesian network version of SPAR-H, Reliab. Eng. Syst. Saf. 115 (2013) 33-42. https://doi.org/10.1016/j.ress.2013.02.015
  21. W.J. Galyean, Orthogonal PSF taxonomy for human reliability analyses (PSAM-0281), in: Proceedings of the Eighth International Conference on Probabilistic Safety Assessment & Management (PSAM), ASME Press, 2006.
  22. J. Park, et al., Step complexity measure for emergency operating proceduresdetermining weighting factors, Nucl. Technol. 143 (3) (2003) 290-308. https://doi.org/10.13182/NT03-A3418
  23. V. Di Pasquale, et al., An overview of human reliability analysis techniques in manufacturing operations, in: Operations Management, InTech, 2013.
  24. A. Swain, Accident Sequence Evaluation Program Human Reliability Analysis Procedure, 1987. NUREG/CR-4772.
  25. G. Parry, et al., An Approach to the Analysis of Operator Actions in Probabilistic Risk Assessment, EPRI Report TR-100259, 1992.
  26. J.J. Hox, M.B. Timo, An Introduction to Structural Equation Modeling, 1998.
  27. A.E. Hurley, et al., Exploratory and confirmatory factor analysis: guidelines, issues, and alternatives, J. Organ. Behav. (1997) 667-683.
  28. David M. Magerman, Statistical decision-tree models for parsing, in: Proceedings of the 33rd Annual Meeting on Association for Computational Linguistics, Association for Computational Linguistics, 1995.
  29. KINS. Operational Information System Database, http://opis.kins.re.kr/opis?act=KEOPISMAIN.
  30. P.C. Austin, Using the standardized difference to compare the prevalence of a binary variable between two groups in observational research, Commun. Stat. Simulat. Comput. 38 (6) (2009) 1228-1234. https://doi.org/10.1080/03610910902859574
  31. U.S.NRC, NUREG-0711 Human Factors Engineering Program Review Model Revision 3, in: United States Nuclear Regulatory Commission, United States Nuclear Regulatory Commission, Washington, DC, 2012.
  32. M.R. Endsley, Design and evaluation for situation awareness enhancement, in: Proceedings of the Human Factors Society Annual Meeting, SAGE Publications Sage CA, Los Angeles, CA, 1988.
  33. S.W. Lee, et al., Measuring situation awareness of operating team in different main control room environments of nuclear power plants, Nucl. Eng. Technol. 48 (1) (2016) 153-163. https://doi.org/10.1016/j.net.2015.09.008
  34. J.H. Kim, V.N. Dang, Impact of Advanced Alarm Systems and Information Displays on Human Reliability in the Digital Control Room of Nuclear Power Plants, 2011.
  35. J. O'Hara, et al., The effects of interface management tasks on crew performance and safety in complex, computer-based systems, Nureg/CR, 2002, p. 6690.
  36. M. Rasmussen, M.I. Standal, K. Laumann, Task complexity as a performance shaping factor: a review and recommendations in standardized plant analysis risk-human reliability analysis (SPAR-H) adaption, Saf. Sci. 76 (2015) 228-238. https://doi.org/10.1016/j.ssci.2015.03.005
  37. K. Groth, L.P. Swiler, Use of a SPAR-H bayesian network for predicting human error probabilities with missing observations, in: 11th International Probabilistic Safety Assessment and Management Conference, 2012.
  38. P. Kline, An Easy Guide to Factor Analysis, Routledge, 2014.
  39. R.L. Boring, H.S. Blackman, The origins of the SPAR-H method's performance shaping factor multipliers, in: Human Factors and Power Plants and HPRCT 13th Annual Meeting, 2007 IEEE 8th, IEEE, 2007.
  40. U.S.NRC, Evaluation of Human Reliability Analysis Methods against Good Practices, NUREG-1842, NRC, Washington, 2006.
  41. W. Jung, et al., A framework of HRA data collection in nuclear power plants, in: The Thirteenth International Conference on Probabilistic Safety Assessment and Management vol. 13, PSAM, 2016.
  42. Jooyoung Park, et al., A comparison of the quantification aspects of human reliability analysis methods in nuclear power plants, Ann. Nucl. Energy 133 (2019) 297-312. https://doi.org/10.1016/j.anucene.2019.05.031
  43. Yochan Kim, et al., A statistical approach to estimating effects of performance shaping factors on human error probabilities of soft controls, Reliab. Eng. Syst. Saf. 142 (2015) 378-387. https://doi.org/10.1016/j.ress.2015.06.004

Cited by

  1. Identifying and clustering performance shaping factors for nuclear power plant commissioning tasks vol.31, pp.1, 2021, https://doi.org/10.1002/hfm.20868
  2. An empirical investigation on association between human factors, ergonomics and lean manufacturing vol.32, pp.16, 2020, https://doi.org/10.1080/09537287.2020.1810815
  3. Weighing and prioritization of individual factors affecting the performance of industries firefighters vol.127, 2020, https://doi.org/10.1016/j.firesaf.2021.103512