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A hybrid model for learning from failures

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A hybrid model for learning from failures. / Stephen, Calvin; Labib, Ashraf.

In: Expert Systems with Applications, Vol. 93, 01.03.2018, p. 212-222.

Research output: Contribution to journalArticle

Harvard

Stephen, C & Labib, A 2018, 'A hybrid model for learning from failures', Expert Systems with Applications, vol. 93, pp. 212-222. https://doi.org/10.1016/j.eswa.2017.10.031

APA

Stephen, C., & Labib, A. (2018). A hybrid model for learning from failures. Expert Systems with Applications, 93, 212-222. https://doi.org/10.1016/j.eswa.2017.10.031

Vancouver

Stephen C, Labib A. A hybrid model for learning from failures. Expert Systems with Applications. 2018 Mar 1;93:212-222. https://doi.org/10.1016/j.eswa.2017.10.031

Author

Stephen, Calvin ; Labib, Ashraf. / A hybrid model for learning from failures. In: Expert Systems with Applications. 2018 ; Vol. 93. pp. 212-222.

Bibtex

@article{1ab42bf6e8c44e04a76e311887233496,
title = "A hybrid model for learning from failures",
abstract = "In this paper we propose the usage of a hybrid of techniques as complementary tools in decision analysis for learning from failures and the reason behind systems failure. We demonstrate the applicability of these tools through an aviation case study, where an accident investigation report was obtained from the Directorate of Accident Investigation in the Ministry of Transport and Communications in Botswana to provide as a basis for the application of the model. The report included all the factual information required to carry out the investigation using the hybrid of FTA, RBD, AHP, HoQ and the DMG tools.We discuss the steps followed in applying the tools in the process of learning from failure. It also shows the importance of such tools in accident investigations by showing the importance of prioritising the available options in order of their importance to the accident under investigation.Most of the available research in learning from failure focuses mostly on the direct causal factors of the failure event. Here we provide a holistic approach to learning from failure by focusing on both direct and indirect causes of a failure event through the use of Reliability Engineering tools, Multi Criteria Decision Making tools and House of Quality.",
keywords = "decision analysis, multiple criteria, decision making, RCUK, ESRC, ES/N009614/1",
author = "Calvin Stephen and Ashraf Labib",
year = "2018",
month = "3",
day = "1",
doi = "10.1016/j.eswa.2017.10.031",
language = "English",
volume = "93",
pages = "212--222",
journal = "Expert Systems with Applications",
issn = "0957-4174",
publisher = "Elsevier Limited",

}

RIS

TY - JOUR

T1 - A hybrid model for learning from failures

AU - Stephen, Calvin

AU - Labib, Ashraf

PY - 2018/3/1

Y1 - 2018/3/1

N2 - In this paper we propose the usage of a hybrid of techniques as complementary tools in decision analysis for learning from failures and the reason behind systems failure. We demonstrate the applicability of these tools through an aviation case study, where an accident investigation report was obtained from the Directorate of Accident Investigation in the Ministry of Transport and Communications in Botswana to provide as a basis for the application of the model. The report included all the factual information required to carry out the investigation using the hybrid of FTA, RBD, AHP, HoQ and the DMG tools.We discuss the steps followed in applying the tools in the process of learning from failure. It also shows the importance of such tools in accident investigations by showing the importance of prioritising the available options in order of their importance to the accident under investigation.Most of the available research in learning from failure focuses mostly on the direct causal factors of the failure event. Here we provide a holistic approach to learning from failure by focusing on both direct and indirect causes of a failure event through the use of Reliability Engineering tools, Multi Criteria Decision Making tools and House of Quality.

AB - In this paper we propose the usage of a hybrid of techniques as complementary tools in decision analysis for learning from failures and the reason behind systems failure. We demonstrate the applicability of these tools through an aviation case study, where an accident investigation report was obtained from the Directorate of Accident Investigation in the Ministry of Transport and Communications in Botswana to provide as a basis for the application of the model. The report included all the factual information required to carry out the investigation using the hybrid of FTA, RBD, AHP, HoQ and the DMG tools.We discuss the steps followed in applying the tools in the process of learning from failure. It also shows the importance of such tools in accident investigations by showing the importance of prioritising the available options in order of their importance to the accident under investigation.Most of the available research in learning from failure focuses mostly on the direct causal factors of the failure event. Here we provide a holistic approach to learning from failure by focusing on both direct and indirect causes of a failure event through the use of Reliability Engineering tools, Multi Criteria Decision Making tools and House of Quality.

KW - decision analysis

KW - multiple criteria

KW - decision making

KW - RCUK

KW - ESRC

KW - ES/N009614/1

U2 - 10.1016/j.eswa.2017.10.031

DO - 10.1016/j.eswa.2017.10.031

M3 - Article

VL - 93

SP - 212

EP - 222

JO - Expert Systems with Applications

JF - Expert Systems with Applications

SN - 0957-4174

ER -

ID: 8039448