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Resilience analysis: A formulation to model risk factors on complex system resilience
Resilience is about the ability of the system to resist, adapt to, and expeditiously recover from a disruptive event. The first and maybe the crucial step of resilience management is known as resilience analysis. However, there are many obstacles in front of the analyzers to analyze the resilience o...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Springer India
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8130812/ http://dx.doi.org/10.1007/s13198-021-01131-w |
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author | Mottahedi, Adel Sereshki, Farhang Ataei, Mohammad Nouri Qarahasanlou, Ali Barabadi, Abbas |
author_facet | Mottahedi, Adel Sereshki, Farhang Ataei, Mohammad Nouri Qarahasanlou, Ali Barabadi, Abbas |
author_sort | Mottahedi, Adel |
collection | PubMed |
description | Resilience is about the ability of the system to resist, adapt to, and expeditiously recover from a disruptive event. The first and maybe the crucial step of resilience management is known as resilience analysis. However, there are many obstacles in front of the analyzers to analyze the resilience of systems. One of these obstacles is precise resilience data accessibility. Conventional resilience analysis methods frequently only consider historical data (e.g., time to repair and time to failure). However, to analyze the system resilience more precisely, the effect of the risk factors, which are known as observed and unobserved covariates, should be considered in the collected resilience database. These covariates will lead to the observed and unobserved heterogeneities among the collected database. Ignoring the effect of covariate may lead to erroneous conclusion about the resilience level of the system. Since it is hard to find a homogeneous operating condition, in this study, a formulation is proposed to model the effect of these covariates on complex system resilience. Finally, it is applied to a transportation system of a surface mine. |
format | Online Article Text |
id | pubmed-8130812 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | Springer India |
record_format | MEDLINE/PubMed |
spelling | pubmed-81308122021-05-19 Resilience analysis: A formulation to model risk factors on complex system resilience Mottahedi, Adel Sereshki, Farhang Ataei, Mohammad Nouri Qarahasanlou, Ali Barabadi, Abbas Int J Syst Assur Eng Manag Original Article Resilience is about the ability of the system to resist, adapt to, and expeditiously recover from a disruptive event. The first and maybe the crucial step of resilience management is known as resilience analysis. However, there are many obstacles in front of the analyzers to analyze the resilience of systems. One of these obstacles is precise resilience data accessibility. Conventional resilience analysis methods frequently only consider historical data (e.g., time to repair and time to failure). However, to analyze the system resilience more precisely, the effect of the risk factors, which are known as observed and unobserved covariates, should be considered in the collected resilience database. These covariates will lead to the observed and unobserved heterogeneities among the collected database. Ignoring the effect of covariate may lead to erroneous conclusion about the resilience level of the system. Since it is hard to find a homogeneous operating condition, in this study, a formulation is proposed to model the effect of these covariates on complex system resilience. Finally, it is applied to a transportation system of a surface mine. Springer India 2021-05-18 2021 /pmc/articles/PMC8130812/ http://dx.doi.org/10.1007/s13198-021-01131-w Text en © The Society for Reliability Engineering, Quality and Operations Management (SREQOM), India and The Division of Operation and Maintenance, Lulea University of Technology, Sweden 2021 This article is made available via the PMC Open Access Subset for unrestricted research re-use and secondary analysis in any form or by any means with acknowledgement of the original source. These permissions are granted for the duration of the World Health Organization (WHO) declaration of COVID-19 as a global pandemic. |
spellingShingle | Original Article Mottahedi, Adel Sereshki, Farhang Ataei, Mohammad Nouri Qarahasanlou, Ali Barabadi, Abbas Resilience analysis: A formulation to model risk factors on complex system resilience |
title | Resilience analysis: A formulation to model risk factors on complex system resilience |
title_full | Resilience analysis: A formulation to model risk factors on complex system resilience |
title_fullStr | Resilience analysis: A formulation to model risk factors on complex system resilience |
title_full_unstemmed | Resilience analysis: A formulation to model risk factors on complex system resilience |
title_short | Resilience analysis: A formulation to model risk factors on complex system resilience |
title_sort | resilience analysis: a formulation to model risk factors on complex system resilience |
topic | Original Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8130812/ http://dx.doi.org/10.1007/s13198-021-01131-w |
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