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dc.contributor.author
Greco, Salvatore F.
dc.contributor.author
Podofillini, Luca
dc.contributor.author
Dang, Vinh N.
dc.date.accessioned
2021-01-28T16:29:08Z
dc.date.available
2021-01-28T16:07:40Z
dc.date.available
2021-01-28T16:29:08Z
dc.date.issued
2021-02
dc.identifier.issn
0951-8320
dc.identifier.other
10.1016/j.ress.2020.107309
en_US
dc.identifier.uri
http://hdl.handle.net/20.500.11850/466448
dc.identifier.doi
10.3929/ethz-b-000466448
dc.description.abstract
The models adopted in Human Reliability Analysis (HRA) characterize personnel tasks and performance conditions via categories of task and influencing factors (e.g. task types and Performance Shaping Factors, PSF). These categories cover the variability of the operational tasks and conditions affecting performance, and of the associated Human Error Probability (HEP). However, variability exists as well within such categories, for example because of the different scenarios and plants in which data is collected, as well as of the operating crew differences (within-category and crew-to-crew variability). This paper presents a Bayesian model to mathematically aggregate simulator data to estimate failure probabilities, explicitly accounting for the specific tasks, scenarios, plants and crew behavior variability, within a given “constellation” (i.e. combination) of task and factor categories. The general aim of the proposed work is to provide future HRA with reference data with stronger empirical basis for failure probability values, both for their nominal values as well as for their variability and uncertainty. Numerical applications with both artificially-generated data and real simulator data are provided to demonstrate the effects of modelling variability in HEP estimates, to avoid potential overconfidence and biases. The applicability of the proposed model to ongoing simulator data collection programs is also investigated.
en_US
dc.format
application/pdf
en_US
dc.language.iso
en
en_US
dc.publisher
Elsevier
en_US
dc.rights.uri
http://creativecommons.org/licenses/by-nc-nd/4.0/
dc.subject
Human reliability analysis
en_US
dc.subject
Simulator data
en_US
dc.subject
Performance variability
en_US
dc.subject
SACADA
en_US
dc.subject
HuREX
en_US
dc.subject
Bayesian inference
en_US
dc.title
A Bayesian model to treat within-category and crew-to-crew variability in simulator data for Human Reliability Analysis
en_US
dc.type
Journal Article
dc.rights.license
Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International
dc.date.published
2020-11-13
ethz.journal.title
Reliability Engineering & System Safety
ethz.journal.volume
206
en_US
ethz.journal.abbreviated
Reliab. eng. syst. saf.
ethz.pages.start
107309
en_US
ethz.size
19 p.
en_US
ethz.version.deposit
publishedVersion
en_US
ethz.publication.place
London
en_US
ethz.publication.status
published
en_US
ethz.leitzahl
ETH Zürich::00002 - ETH Zürich::00012 - Lehre und Forschung::00007 - Departemente::02130 - Dep. Maschinenbau und Verfahrenstechnik / Dep. of Mechanical and Process Eng.::02668 - Inst. f. Energie- und Verfahrenstechnik / Inst. Energy and Process Engineering::03725 - Prasser, Horst-Michael (emeritus) / Prasser, Horst-Michael (emeritus)
en_US
ethz.leitzahl.certified
ETH Zürich::00002 - ETH Zürich::00012 - Lehre und Forschung::00007 - Departemente::02130 - Dep. Maschinenbau und Verfahrenstechnik / Dep. of Mechanical and Process Eng.::02668 - Inst. f. Energie- und Verfahrenstechnik / Inst. Energy and Process Engineering::03725 - Prasser, Horst-Michael (emeritus) / Prasser, Horst-Michael (emeritus)
en_US
ethz.date.deposited
2021-01-28T16:07:47Z
ethz.source
FORM
ethz.eth
yes
en_US
ethz.availability
Open access
en_US
ethz.rosetta.installDate
2021-01-28T16:29:16Z
ethz.rosetta.lastUpdated
2022-03-29T05:00:36Z
ethz.rosetta.versionExported
true
ethz.COinS
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