• An abnormal situation modeling...

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

Title:
An abnormal situation modeling method to assist operators in safety-critical systems
Authors:
NADERPOUR, Mohsen, JIE LU, GUANGQUAN ZHANG
Source:
Reliability engineering & systems safety. 133:33-47
Publisher Information:
Oxford: Elsevier, 2015.
Publication Year:
2015
Physical Description:
print, 30 ref
Original Material:
INIST-CNRS
Subject Terms:
Control theory, operational research, Automatique, recherche opérationnelle, Energy, Énergie, Sciences exactes et technologie, Exact sciences and technology, Sciences appliquees, Applied sciences, Recherche operationnelle. Gestion, Operational research. Management science, Recherche opérationnelle et modèles formalisés de gestion, Operational research and scientific management, Théorie du risque. Assurance, Risk theory. Actuarial science, Informatique; automatique theorique; systemes, Computer science; control theory; systems, Informatique théorique, Theoretical computing, Recherche information. Graphe, Information retrieval. Graph, Logiciel, Software, Systèmes informatiques et systèmes répartis. Interface utilisateur, Computer systems and distributed systems. User interface, Accident, Accidente, Analyse risque, Risk analysis, Análisis riesgo, Analyse scène, Scene analysis, Análisis escena, Anomalie, Anomaly, Anomalía, Charge mentale, Mental load, Carga mental, Erreur humaine, Human error, Error humano, Evaluation risque, Risk assessment, Indice aptitude, Capability index, Indice aptitud, Interface utilisateur, User interface, Interfase usuario, Logique floue, Fuzzy logic, Lógica difusa, Modélisation, Modeling, Modelización, Méthode graphe, Graph method, Método grafo, Prise conscience, Awareness, Toma de conciencia, Réseau Bayes, Bayes network, Red Bayes, Réseau probabiliste, Probabilistic net, Red probabilista, Sensibilité contexte, Context aware, Sensibilidad contexto, Système critique, Critical system, Sistema crítica, Usine chimique, Chemical plant, Fábrica productos químicos, Bayesian networks, Fuzzy logic systems, Safety-critical systems, Situation assessment, Situation awareness
Document Type:
Academic journal Article
File Description:
text
Language:
English
Author Affiliations:
Decision Systems and e-Service Intelligence Laboratory, Centre for Quantum Computation & Intelligent Systems, School of Software, Faculty of Engineering and IT, University of Technology Sydney, PO Box 123, Broadway, NSW 2007, Australia
ISSN:
0951-8320
Access URL:
http://pascal-francis.inist.fr/vibad/index.php?action=search&terms=28887250
Rights:
Copyright 2015 INIST-CNRS
CC BY 4.0
Sauf mention contraire ci-dessus, le contenu de cette notice bibliographique peut être utilisé dans le cadre d’une licence CC BY 4.0 Inist-CNRS / Unless otherwise stated above, the content of this bibliographic record may be used under a CC BY 4.0 licence by Inist-CNRS / A menos que se haya señalado antes, el contenido de este registro bibliográfico puede ser utilizado al amparo de una licencia CC BY 4.0 Inist-CNRS
Notes:
Computer science; theoretical automation; systems

Operational research. Management
Accession Number:
edscal.28887250
Database:
PASCAL Archive

Further Information

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.