A novel qualitative prospective methodology to assess human error during accident sequences

Autores
Calvo Olivares, Romina Daniela; Rivera, Selva Soledad; Núñez Mc Leod, Jorge Eduardo
Año de publicación
2018
Idioma
inglés
Tipo de recurso
artículo
Estado
versión publicada
Descripción
Numerous theoretical models and techniques to assess human error were developed since the 60's. Most of these models were developed for the nuclear, military, and aviation sectors. These methods have the following weaknesses that limit their use in industry: the lack of analysis of underlying causal cognitive mechanisms, need of retrospective data for implementation, strong dependence on expert judgment, focus on a particular type of error, and/or analysis of operator behaviour and decision-making without considering the role of the system in such decisions. The purpose of the present research is to develop a qualitative prospective methodology that does not depend exclusively on retrospective information, that does not require expert judgment for implementation and that allows predicting potential sequences of accidents before they occur. It has been proposed for new (or existent) small and medium- scale facilities, whose processes are simple. To the best of our knowledge, a methodology that meets these requirements has not been reported in literature thus far. The methodology proposed in this study was applied to the methanol storage area of a biodiesel facility. It could predict potential sequences of accidents, through the analysis of information provided by different system devices and the study of the possible deviations of operators in decision-making. It also enabled the identification of the shortcomings in the human-machine interface and proposed an optimization of the current configuration.
Fil: Calvo Olivares, Romina Daniela. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Universidad Nacional de Cuyo. Facultad de Ingenieria. Instituto de Capacitación Especial y Desarrollo de Ingeniería Asistida por Computadora; Argentina
Fil: Rivera, Selva Soledad. Universidad Nacional de Cuyo. Facultad de Ingenieria. Instituto de Capacitación Especial y Desarrollo de Ingeniería Asistida por Computadora; Argentina
Fil: Núñez Mc Leod, Jorge Eduardo. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Universidad Nacional de Cuyo. Facultad de Ingenieria. Instituto de Capacitación Especial y Desarrollo de Ingeniería Asistida por Computadora; Argentina
Materia
ACCIDENT SEQUENCES
HUMAN ERROR
HUMAN RELIABILITY ASSESSMENT
HUMAN-MACHINE INTERFACE
QUALITATIVE PROSPECTIVE MODEL
Nivel de accesibilidad
acceso abierto
Condiciones de uso
https://creativecommons.org/licenses/by-nc-nd/2.5/ar/
Repositorio
CONICET Digital (CONICET)
Institución
Consejo Nacional de Investigaciones Científicas y Técnicas
OAI Identificador
oai:ri.conicet.gov.ar:11336/96320
Acceder
id CONICETDig_fdec6cb0dc34e7590d94623c4e8bc5c3
oai_identifier_str oai:ri.conicet.gov.ar:11336/96320
network_acronym_str CONICETDig
repository_id_str 3498
network_name_str CONICET Digital (CONICET)
spelling A novel qualitative prospective methodology to assess human error during accident sequencesCalvo Olivares, Romina DanielaRivera, Selva SoledadNúñez Mc Leod, Jorge EduardoACCIDENT SEQUENCESHUMAN ERRORHUMAN RELIABILITY ASSESSMENTHUMAN-MACHINE INTERFACEQUALITATIVE PROSPECTIVE MODELhttps://purl.org/becyt/ford/2.11https://purl.org/becyt/ford/2Numerous theoretical models and techniques to assess human error were developed since the 60's. Most of these models were developed for the nuclear, military, and aviation sectors. These methods have the following weaknesses that limit their use in industry: the lack of analysis of underlying causal cognitive mechanisms, need of retrospective data for implementation, strong dependence on expert judgment, focus on a particular type of error, and/or analysis of operator behaviour and decision-making without considering the role of the system in such decisions. The purpose of the present research is to develop a qualitative prospective methodology that does not depend exclusively on retrospective information, that does not require expert judgment for implementation and that allows predicting potential sequences of accidents before they occur. It has been proposed for new (or existent) small and medium- scale facilities, whose processes are simple. To the best of our knowledge, a methodology that meets these requirements has not been reported in literature thus far. The methodology proposed in this study was applied to the methanol storage area of a biodiesel facility. It could predict potential sequences of accidents, through the analysis of information provided by different system devices and the study of the possible deviations of operators in decision-making. It also enabled the identification of the shortcomings in the human-machine interface and proposed an optimization of the current configuration.Fil: Calvo Olivares, Romina Daniela. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Universidad Nacional de Cuyo. Facultad de Ingenieria. Instituto de Capacitación Especial y Desarrollo de Ingeniería Asistida por Computadora; ArgentinaFil: Rivera, Selva Soledad. Universidad Nacional de Cuyo. Facultad de Ingenieria. Instituto de Capacitación Especial y Desarrollo de Ingeniería Asistida por Computadora; ArgentinaFil: Núñez Mc Leod, Jorge Eduardo. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Universidad Nacional de Cuyo. Facultad de Ingenieria. Instituto de Capacitación Especial y Desarrollo de Ingeniería Asistida por Computadora; ArgentinaElsevier Science2018-03info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdfapplication/pdfhttp://hdl.handle.net/11336/96320Calvo Olivares, Romina Daniela; Rivera, Selva Soledad; Núñez Mc Leod, Jorge Eduardo; A novel qualitative prospective methodology to assess human error during accident sequences; Elsevier Science; Safety Science; 103; 3-2018; 137-1520925-7535CONICET DigitalCONICETenginfo:eu-repo/semantics/altIdentifier/url/https://www.sciencedirect.com/science/article/pii/S092575351730526Xinfo:eu-repo/semantics/altIdentifier/doi/10.1016/j.ssci.2017.10.023info:eu-repo/semantics/openAccesshttps://creativecommons.org/licenses/by-nc-nd/2.5/ar/reponame:CONICET Digital (CONICET)instname:Consejo Nacional de Investigaciones Científicas y Técnicas2025-09-29T10:01:36Zoai:ri.conicet.gov.ar:11336/96320instacron:CONICETInstitucionalhttp://ri.conicet.gov.ar/Organismo científico-tecnológicoNo correspondehttp://ri.conicet.gov.ar/oai/requestdasensio@conicet.gov.ar; lcarlino@conicet.gov.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:34982025-09-29 10:01:36.899CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicasfalse
dc.title.none.fl_str_mv A novel qualitative prospective methodology to assess human error during accident sequences
title A novel qualitative prospective methodology to assess human error during accident sequences
spellingShingle A novel qualitative prospective methodology to assess human error during accident sequences
Calvo Olivares, Romina Daniela
ACCIDENT SEQUENCES
HUMAN ERROR
HUMAN RELIABILITY ASSESSMENT
HUMAN-MACHINE INTERFACE
QUALITATIVE PROSPECTIVE MODEL
title_short A novel qualitative prospective methodology to assess human error during accident sequences
title_full A novel qualitative prospective methodology to assess human error during accident sequences
title_fullStr A novel qualitative prospective methodology to assess human error during accident sequences
title_full_unstemmed A novel qualitative prospective methodology to assess human error during accident sequences
title_sort A novel qualitative prospective methodology to assess human error during accident sequences
dc.creator.none.fl_str_mv Calvo Olivares, Romina Daniela
Rivera, Selva Soledad
Núñez Mc Leod, Jorge Eduardo
author Calvo Olivares, Romina Daniela
author_facet Calvo Olivares, Romina Daniela
Rivera, Selva Soledad
Núñez Mc Leod, Jorge Eduardo
author_role author
author2 Rivera, Selva Soledad
Núñez Mc Leod, Jorge Eduardo
author2_role author
author
dc.subject.none.fl_str_mv ACCIDENT SEQUENCES
HUMAN ERROR
HUMAN RELIABILITY ASSESSMENT
HUMAN-MACHINE INTERFACE
QUALITATIVE PROSPECTIVE MODEL
topic ACCIDENT SEQUENCES
HUMAN ERROR
HUMAN RELIABILITY ASSESSMENT
HUMAN-MACHINE INTERFACE
QUALITATIVE PROSPECTIVE MODEL
purl_subject.fl_str_mv https://purl.org/becyt/ford/2.11
https://purl.org/becyt/ford/2
dc.description.none.fl_txt_mv Numerous theoretical models and techniques to assess human error were developed since the 60's. Most of these models were developed for the nuclear, military, and aviation sectors. These methods have the following weaknesses that limit their use in industry: the lack of analysis of underlying causal cognitive mechanisms, need of retrospective data for implementation, strong dependence on expert judgment, focus on a particular type of error, and/or analysis of operator behaviour and decision-making without considering the role of the system in such decisions. The purpose of the present research is to develop a qualitative prospective methodology that does not depend exclusively on retrospective information, that does not require expert judgment for implementation and that allows predicting potential sequences of accidents before they occur. It has been proposed for new (or existent) small and medium- scale facilities, whose processes are simple. To the best of our knowledge, a methodology that meets these requirements has not been reported in literature thus far. The methodology proposed in this study was applied to the methanol storage area of a biodiesel facility. It could predict potential sequences of accidents, through the analysis of information provided by different system devices and the study of the possible deviations of operators in decision-making. It also enabled the identification of the shortcomings in the human-machine interface and proposed an optimization of the current configuration.
Fil: Calvo Olivares, Romina Daniela. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Universidad Nacional de Cuyo. Facultad de Ingenieria. Instituto de Capacitación Especial y Desarrollo de Ingeniería Asistida por Computadora; Argentina
Fil: Rivera, Selva Soledad. Universidad Nacional de Cuyo. Facultad de Ingenieria. Instituto de Capacitación Especial y Desarrollo de Ingeniería Asistida por Computadora; Argentina
Fil: Núñez Mc Leod, Jorge Eduardo. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Universidad Nacional de Cuyo. Facultad de Ingenieria. Instituto de Capacitación Especial y Desarrollo de Ingeniería Asistida por Computadora; Argentina
description Numerous theoretical models and techniques to assess human error were developed since the 60's. Most of these models were developed for the nuclear, military, and aviation sectors. These methods have the following weaknesses that limit their use in industry: the lack of analysis of underlying causal cognitive mechanisms, need of retrospective data for implementation, strong dependence on expert judgment, focus on a particular type of error, and/or analysis of operator behaviour and decision-making without considering the role of the system in such decisions. The purpose of the present research is to develop a qualitative prospective methodology that does not depend exclusively on retrospective information, that does not require expert judgment for implementation and that allows predicting potential sequences of accidents before they occur. It has been proposed for new (or existent) small and medium- scale facilities, whose processes are simple. To the best of our knowledge, a methodology that meets these requirements has not been reported in literature thus far. The methodology proposed in this study was applied to the methanol storage area of a biodiesel facility. It could predict potential sequences of accidents, through the analysis of information provided by different system devices and the study of the possible deviations of operators in decision-making. It also enabled the identification of the shortcomings in the human-machine interface and proposed an optimization of the current configuration.
publishDate 2018
dc.date.none.fl_str_mv 2018-03
dc.type.none.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
http://purl.org/coar/resource_type/c_6501
info:ar-repo/semantics/articulo
format article
status_str publishedVersion
dc.identifier.none.fl_str_mv http://hdl.handle.net/11336/96320
Calvo Olivares, Romina Daniela; Rivera, Selva Soledad; Núñez Mc Leod, Jorge Eduardo; A novel qualitative prospective methodology to assess human error during accident sequences; Elsevier Science; Safety Science; 103; 3-2018; 137-152
0925-7535
CONICET Digital
CONICET
url http://hdl.handle.net/11336/96320
identifier_str_mv Calvo Olivares, Romina Daniela; Rivera, Selva Soledad; Núñez Mc Leod, Jorge Eduardo; A novel qualitative prospective methodology to assess human error during accident sequences; Elsevier Science; Safety Science; 103; 3-2018; 137-152
0925-7535
CONICET Digital
CONICET
dc.language.none.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv info:eu-repo/semantics/altIdentifier/url/https://www.sciencedirect.com/science/article/pii/S092575351730526X
info:eu-repo/semantics/altIdentifier/doi/10.1016/j.ssci.2017.10.023
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
https://creativecommons.org/licenses/by-nc-nd/2.5/ar/
eu_rights_str_mv openAccess
rights_invalid_str_mv https://creativecommons.org/licenses/by-nc-nd/2.5/ar/
dc.format.none.fl_str_mv application/pdf
application/pdf
dc.publisher.none.fl_str_mv Elsevier Science
publisher.none.fl_str_mv Elsevier Science
dc.source.none.fl_str_mv reponame:CONICET Digital (CONICET)
instname:Consejo Nacional de Investigaciones Científicas y Técnicas
reponame_str CONICET Digital (CONICET)
collection CONICET Digital (CONICET)
instname_str Consejo Nacional de Investigaciones Científicas y Técnicas
repository.name.fl_str_mv CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicas
repository.mail.fl_str_mv dasensio@conicet.gov.ar; lcarlino@conicet.gov.ar
_version_ 1844613811771801600
score 13.070432

Publicaciones similares