An evaluation method for HMI of deep-sea manned submersible based on human reliability.
Journal
Scientific reports
ISSN: 2045-2322
Titre abrégé: Sci Rep
Pays: England
ID NLM: 101563288
Informations de publication
Date de publication:
04 09 2023
04 09 2023
Historique:
received:
24
03
2023
accepted:
21
08
2023
medline:
6
9
2023
pubmed:
5
9
2023
entrez:
4
9
2023
Statut:
epublish
Résumé
Improving the human reliability of the human-machine interface (HMI) of deep-sea manned submersible is of great importance for the development of the deep-sea field. Based on the SHEL (Software S, Hardware H, Environment E, Liveware L) model, this study classifies the performance shaping factors (PSF) that affect the human reliability of submersible HMIs and builds a PSF system. The interpretative structural model (ISM) is used to matrix the interactions between the elements that make up the system of PSF. A multi-level recursive structure is obtained by building the corresponding adjacency matrix. The Noisy-OR model is introduced to construct a Bayesian network in order to build a new HMI evaluation method. A real case of Bayesian network causal inference verifies the validity of the built method. This study proposes a set of HMI human reliability evaluation methods applicable to deep-sea manned submersible, which provides a new idea for human reliability assessment.
Identifiants
pubmed: 37666861
doi: 10.1038/s41598-023-41063-y
pii: 10.1038/s41598-023-41063-y
pmc: PMC10477282
doi:
Types de publication
Journal Article
Research Support, Non-U.S. Gov't
Langues
eng
Sous-ensembles de citation
IM
Pagination
14507Informations de copyright
© 2023. Springer Nature Limited.
Références
Appl Ergon. 2018 Jul;70:6-17
pubmed: 29866327
Sci Rep. 2021 Aug 26;11(1):17284
pubmed: 34446795
J Safety Res. 2022 Feb;80:198-214
pubmed: 35249600