An abnormal situation modeling method to assist operators in safety-critical systems

Naderpour, M; Lu, J; Zhang, G

An abnormal situation modeling method to assist operators in safety-critical systems

Naderpour, M

Lu, J

Zhang, G

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Publication Type:: Journal Article
Citation:: Reliability Engineering and System Safety, 2015, 133 pp. 33 - 47
Issue Date:: 2015-01-01

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Field	Value	Language
dc.contributor.author	Naderpour, M https://orcid.org/0000-0002-3549-3875	en_US
dc.contributor.author	Lu, J https://orcid.org/0000-0003-0690-4732	en_US
dc.contributor.author	Zhang, G https://orcid.org/0000-0003-3960-0583	en_US
dc.date.available	2020-05-25T19:06:07Z
dc.date.issued	2015-01-01	en_US
dc.identifier.citation	Reliability Engineering and System Safety, 2015, 133 pp. 33 - 47	en_US
dc.identifier.issn	0951-8320	en_US
dc.identifier.uri	http://hdl.handle.net/10453/32963
dc.description.abstract	© 2014 Elsevier Ltd. One of the main causes of accidents in safety-critical systems is human error. In order to reduce human errors in the process of handling abnormal situations that are highly complex and mentally taxing activities, operators need to be supported, from a cognitive perspective, in order to reduce their workload, stress, and the consequent error rate. Of the various cognitive activities, a correct understanding of the situation, i.e. situation awareness (SA), is a crucial factor in improving performance and reducing errors. Despite the importance of SA in decision-making in time- and safety-critical situations, the difficulty of SA modeling and assessment means that very few methods have as yet been developed. This study confronts this challenge, and develops an innovative abnormal situation modeling (ASM) method that exploits the capabilities of risk indicators, Bayesian networks and fuzzy logic systems. The risk indicators are used to identify abnormal situations, Bayesian networks are utilized to model them and a fuzzy logic system is developed to assess them. The ASM method can be used in the development of situation assessment decision support systems that underlie the achievement of SA. The performance of the ASM method is tested through a real case study at a chemical plant.	en_US
dc.relation	http://purl.org/au-research/grants/arc/DP140101366
dc.relation.ispartof	Reliability Engineering and System Safety	en_US
dc.relation.isbasedon	10.1016/j.ress.2014.08.003	en_US
dc.subject.classification	Strategic, Defence & Security Studies	en_US
dc.title	An abnormal situation modeling method to assist operators in safety-critical systems	en_US
dc.type	Journal Article
utslib.citation.volume	133	en_US
utslib.for	080605 Decision Support and Group Support Systems	en_US
utslib.for	01 Mathematical Sciences	en_US
utslib.for	09 Engineering	en_US
utslib.for	15 Commerce, Management, Tourism and Services	en_US
pubs.embargo.period	Not known	en_US
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Computer Science
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Information, Systems and Modelling
pubs.organisational-group	/University of Technology Sydney/Strength - CAI - Centre for Artificial Intelligence
pubs.organisational-group	/University of Technology Sydney/Strength - CAMGIS - Centre for Advanced Modelling and Geospatial lnformation Systems
utslib.copyright.status	open_access
pubs.publication-status	Published	en_US
pubs.volume	133	en_US

Abstract:

© 2014 Elsevier Ltd. One of the main causes of accidents in safety-critical systems is human error. In order to reduce human errors in the process of handling abnormal situations that are highly complex and mentally taxing activities, operators need to be supported, from a cognitive perspective, in order to reduce their workload, stress, and the consequent error rate. Of the various cognitive activities, a correct understanding of the situation, i.e. situation awareness (SA), is a crucial factor in improving performance and reducing errors. Despite the importance of SA in decision-making in time- and safety-critical situations, the difficulty of SA modeling and assessment means that very few methods have as yet been developed. This study confronts this challenge, and develops an innovative abnormal situation modeling (ASM) method that exploits the capabilities of risk indicators, Bayesian networks and fuzzy logic systems. The risk indicators are used to identify abnormal situations, Bayesian networks are utilized to model them and a fuzzy logic system is developed to assess them. The ASM method can be used in the development of situation assessment decision support systems that underlie the achievement of SA. The performance of the ASM method is tested through a real case study at a chemical plant.

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http://hdl.handle.net/10453/32963