Applications of Dynamic Human Reliability Analysis (dHRA) for Context Aware Operations

Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 778)


Human Reliability Assessment is an essential tool for estimating a human operator’s propensity to commit errors based on available information and estimation of performance shaping factors. Even when estimates are approximate, HRA can provide valuable insights into task performance and vulnerabilities to accidental or volitional human actions. Performance Shaping Factors (PSFs) for Human Reliability Analysis are traditional determined as static across all operating conditions. However, it is likely that the PSFs are dynamic and dependent on the state of the plant. For example, available time, stress, and complexity factors change rapidly if the plant enters abnormal operating conditions. Nuclear Power Plants (NPP) are modernizing control rooms with digital systems and accompanying Human Machine Interfaces (HMIs) to enable operation past their planned 40-year life expectancies. New research is needed to identify how digital HMIs interact with dynamic PSFs to influence Human Error Reliability. Here we examine an experimental paradigm utilizing a microworld with non-expert operators to assess how context-aware HMIs could reduce human error by adapting the presentation of information based on the dynamic PSFs estimated from the plant’s current state.


Human factors Human reliability analysis Nuclear Power Plants Mixed initiative systems Industry 4.0 Context aware HMIs 



This work of authorship was prepared as an account of work sponsored by an agency of the United States Government. Neither the United States Government, nor any agency thereof, nor any of their employees makes any warranty, express or implied, or assumes any legal liability or responsibility for the accuracy, completeness, or usefulness of any information, apparatus, product, or process disclosed, or represents that its use would not infringe privately-owned rights. Idaho National Laboratory is a multi-program laboratory operated by Battelle Energy Alliance LLC, for the United States Department of Energy.


  1. 1.
    Fitts, P., Jones, R.: Analysis of factors contributing to 460 “pilot error” experiences in operating aircraft controls (Report No. TSEAA-694-12). Aero Medical Laboratory, Air Materiel Command, U.S. Air Force, Dayton (1947)Google Scholar
  2. 2.
    Fitts, P., Jones, R.: Psychological aspects of instrument display. Analysis of 270 “pilot-error” experiences in reading and interpreting aircraft instruments (Report No. TSEAA-694-12A). Aero Medical Laboratory, Air Materiel Command, U.S. Air Force, Dayton (1947)Google Scholar
  3. 3.
    Vincente, K.J.: Heeding the legacy of Meister, Brunswik, & Gibson: toward a broader view of human factors research. Hum. Factors J. Hum. Factors Ergon. Soc 39(2), 323–328 (1997)Google Scholar
  4. 4.
    Chapanis, A.: Words, words, words. Hum. Factors 7, 1–17 (1965)CrossRefGoogle Scholar
  5. 5.
    Pew, R.W.: Alphonse Chapanis: pioneer in the application of psychology to engineering design. Assoc. Psychol. Sci. (2010)Google Scholar
  6. 6.
    Derksen, M.: Turning men into machines? Scientific management, industrial psychology, and the “human factor”. J. History Behav. Sci. 50, 148–165 (2014)CrossRefGoogle Scholar
  7. 7.
    Meister, D.: The History of Human Factors and Ergonomics. Lawrence Erlbaum Associates, Mahwah (1999)Google Scholar
  8. 8.
    Davis, T.: Lemon! 60 Heroic failures of Motoring. Random House Australia, Sydney (2004)Google Scholar
  9. 9.
    Hermann, P.O.: Design principles for industrie 4.0 scenarios. In: 49th Hawaii International Conference on System Sciences (HICSS), pp. 3928–3937 (2016)Google Scholar
  10. 10.
    Pirvu, B.C., Zamfirescu, C.B.: Smart factory in the contest of the 4th industrial revolution: challenges and opportunities for Romania. In: IOP Conference Series: Materials Science and Engineering, vol. 227 (2017)Google Scholar
  11. 11.
    Harris, S.: Out of the loop: The human-free future of unmanned aerial vehicles. Board of trustees of the Leland Stanford Junior University (2012)Google Scholar
  12. 12.
    McCarthy, E.: Unmanned operations - where control technology is key, EngineerLive, January 2014.
  13. 13.
    Moray, N., Inagaki, T., Itoh, M.: Adaptive automation, trust, and self-confidence in fault management of time-critical tasks. J. Exp. Psychol. Appl. 6(1), 44–58 (2000)CrossRefGoogle Scholar
  14. 14.
    Randall, T.: Here’s How Electric Cars Will Cause the Next Oil Crisis, Bloomberg (2016).
  15. 15.
    Horvitz, E.: Principles of Mixed-Initiative User Interfaces. In: CHI 99, pp. 15–20 (1999)Google Scholar
  16. 16.
    Borchardt, G.: Mixed-initiative control of intelligent systems. In: Proceedings of the Workshop on Space Telerobotics, vol. 2, pp. 423–432 (1987)Google Scholar
  17. 17.
  18. 18.
    Perez, H.R.: Pushing process limits without compromising safety. Chem. Eng. Trans. (2016)Google Scholar
  19. 19.
    Mosleh, A.: PRA: a perspective on strengths, current limitations, and possible improvements. Nuclear Eng. Technol. 46, 1–10 (2014)CrossRefGoogle Scholar
  20. 20.
    Coyne, K., Siu, N.: Simulation-based analysis for nuclear power plant risk assessment: opportunities and challenges. In: Proceedings of the ANS Embedded Conference on Risk Management for Complex Socio-Technical Systems, Washington DC (2013)Google Scholar
  21. 21.
    Gertman, D., Blackman, H., Marble, J., Byers, J., Smith, C.: The SPAR-H Human Reliability Analysis Method. Nuclear Regulatory Commission (2004)Google Scholar
  22. 22.
    Card, S.K., Moran, T.P., Newell, A.: The keystroke level model for user performance time with interactive systems. Commun. ACM 23(7), 396–410 (1980)CrossRefGoogle Scholar
  23. 23.
    Boring, R.L., Rasmussen, M.: GOMS-HRA: a method for treating subtasks in dynamic human reliability analysis. In: Risk, Reliability and Safety: Innovating Theory and Practice: Proceedings of ESREL 2016, pp. 956–963 (2016)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing AG, part of Springer Nature 2019

Authors and Affiliations

  1. 1.Virtual Technology and DesignUniversity of IdahoMoscowUSA
  2. 2.Human Factors and StatisticsIdaho National LaboratoryIdaho FallsUSA

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