A model-driven approach for incorporating human reliability analysis in early emergency operating procedure development

Nikolaos Papakonstantinou, Markus Porthin, Bryan M. O'Halloran, Douglas L. Van Boussuyt

Research output: Chapter in Book/Report/Conference proceedingConference article in proceedingsScientificpeer-review

2 Citations (Scopus)

Abstract

Current Probabilistic Risk Assessment (PRA) methods analyze operator actions in accident scenarios using Human Reliability Analysis (HRA) methods after Emergency Operating Procedures (EOPs) and complex system design are largely complete. This paper proposes the early Model-based HRA (eMHRA) method that couples PRA, HRA, and EOP development together and shifts analysis earlier into the complex system design process. By moving the development of these related and important steps in complex system design earlier in the design process, significant modifications to the complex system can be made much more inexpensively and consume much less time to address critical issues found in PRA, HRA, and EOP development. Further, EOP developers can benefit from rapid and early feedback from the HRA and PRA information. A software tool was developed to implement the eMHRA method presented in this paper and is demonstrated in the paper. A case study is presented of a subsystem of a generic Pressurized Water Reactor (PWR) civilian nuclear power plant. The case study shows that HRA and EOP insights can be incorporated into PRA models early in the design process to better inform system designers of potential high likelihood failure events in operator actions. The eMHRA method presented in this paper provides a new tool for risk analysts to better predict and understand failure scenario outcomes early in the design process. With this information, engineers will be better able to develop new EOPs and operator interfaces to reduce failure likelihood in due to missed operator recovery actions.
Original languageEnglish
Title of host publicationReliability and Maintainability Symposium (RAMS), 2016 Annual
PublisherInstitute of Electrical and Electronic Engineers IEEE
Pages1-6
ISBN (Electronic)978-1-5090-0249-8, 978-1-5090-0248-1
DOIs
Publication statusPublished - 7 Apr 2016
MoE publication typeA4 Article in a conference publication
EventAnnual Reliability and Maintainability Symposium, RAMS 2016 - Tucson, United States
Duration: 25 Jan 201628 Jan 2016

Conference

ConferenceAnnual Reliability and Maintainability Symposium, RAMS 2016
Abbreviated titleRAMS 2016
CountryUnited States
CityTucson
Period25/01/1628/01/16

Fingerprint

Reliability analysis
Risk assessment
Large scale systems
Systems analysis
Computer operating procedures
Pressurized water reactors
Nuclear power plants
Interfaces (computer)
Accidents
Feedback
Engineers
Recovery

Keywords

  • HEP
  • HRA
  • Human Error Probabilities
  • Human Reliability Analysis
  • PRA
  • Probabilistic Risk Assessment

Cite this

Papakonstantinou, N., Porthin, M., O'Halloran, B. M., & Van Boussuyt, D. L. (2016). A model-driven approach for incorporating human reliability analysis in early emergency operating procedure development. In Reliability and Maintainability Symposium (RAMS), 2016 Annual (pp. 1-6). [7447977] Institute of Electrical and Electronic Engineers IEEE. https://doi.org/10.1109/RAMS.2016.7447977
Papakonstantinou, Nikolaos ; Porthin, Markus ; O'Halloran, Bryan M. ; Van Boussuyt, Douglas L. / A model-driven approach for incorporating human reliability analysis in early emergency operating procedure development. Reliability and Maintainability Symposium (RAMS), 2016 Annual. Institute of Electrical and Electronic Engineers IEEE, 2016. pp. 1-6
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Papakonstantinou, N, Porthin, M, O'Halloran, BM & Van Boussuyt, DL 2016, A model-driven approach for incorporating human reliability analysis in early emergency operating procedure development. in Reliability and Maintainability Symposium (RAMS), 2016 Annual., 7447977, Institute of Electrical and Electronic Engineers IEEE, pp. 1-6, Annual Reliability and Maintainability Symposium, RAMS 2016, Tucson, United States, 25/01/16. https://doi.org/10.1109/RAMS.2016.7447977

A model-driven approach for incorporating human reliability analysis in early emergency operating procedure development. / Papakonstantinou, Nikolaos; Porthin, Markus; O'Halloran, Bryan M.; Van Boussuyt, Douglas L.

Reliability and Maintainability Symposium (RAMS), 2016 Annual. Institute of Electrical and Electronic Engineers IEEE, 2016. p. 1-6 7447977.

Research output: Chapter in Book/Report/Conference proceedingConference article in proceedingsScientificpeer-review

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N2 - Current Probabilistic Risk Assessment (PRA) methods analyze operator actions in accident scenarios using Human Reliability Analysis (HRA) methods after Emergency Operating Procedures (EOPs) and complex system design are largely complete. This paper proposes the early Model-based HRA (eMHRA) method that couples PRA, HRA, and EOP development together and shifts analysis earlier into the complex system design process. By moving the development of these related and important steps in complex system design earlier in the design process, significant modifications to the complex system can be made much more inexpensively and consume much less time to address critical issues found in PRA, HRA, and EOP development. Further, EOP developers can benefit from rapid and early feedback from the HRA and PRA information. A software tool was developed to implement the eMHRA method presented in this paper and is demonstrated in the paper. A case study is presented of a subsystem of a generic Pressurized Water Reactor (PWR) civilian nuclear power plant. The case study shows that HRA and EOP insights can be incorporated into PRA models early in the design process to better inform system designers of potential high likelihood failure events in operator actions. The eMHRA method presented in this paper provides a new tool for risk analysts to better predict and understand failure scenario outcomes early in the design process. With this information, engineers will be better able to develop new EOPs and operator interfaces to reduce failure likelihood in due to missed operator recovery actions.

AB - Current Probabilistic Risk Assessment (PRA) methods analyze operator actions in accident scenarios using Human Reliability Analysis (HRA) methods after Emergency Operating Procedures (EOPs) and complex system design are largely complete. This paper proposes the early Model-based HRA (eMHRA) method that couples PRA, HRA, and EOP development together and shifts analysis earlier into the complex system design process. By moving the development of these related and important steps in complex system design earlier in the design process, significant modifications to the complex system can be made much more inexpensively and consume much less time to address critical issues found in PRA, HRA, and EOP development. Further, EOP developers can benefit from rapid and early feedback from the HRA and PRA information. A software tool was developed to implement the eMHRA method presented in this paper and is demonstrated in the paper. A case study is presented of a subsystem of a generic Pressurized Water Reactor (PWR) civilian nuclear power plant. The case study shows that HRA and EOP insights can be incorporated into PRA models early in the design process to better inform system designers of potential high likelihood failure events in operator actions. The eMHRA method presented in this paper provides a new tool for risk analysts to better predict and understand failure scenario outcomes early in the design process. With this information, engineers will be better able to develop new EOPs and operator interfaces to reduce failure likelihood in due to missed operator recovery actions.

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PB - Institute of Electrical and Electronic Engineers IEEE

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Papakonstantinou N, Porthin M, O'Halloran BM, Van Boussuyt DL. A model-driven approach for incorporating human reliability analysis in early emergency operating procedure development. In Reliability and Maintainability Symposium (RAMS), 2016 Annual. Institute of Electrical and Electronic Engineers IEEE. 2016. p. 1-6. 7447977 https://doi.org/10.1109/RAMS.2016.7447977