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dc.contributor.authorYodo, Nita
dc.contributor.authorWang, Pingfeng
dc.date.accessioned2016-03-11T21:41:53Z
dc.date.available2016-03-11T21:41:53Z
dc.date.issued2016-01-20
dc.identifier.citationYodo N, Wang P. Resilience Modeling and Quantification for Engineered Systems Using Bayesian Networks. ASME. J. Mech. Des. 2016;138(3):031404-031404-12. doi:10.1115/1.4032399en_US
dc.identifier.issn1050-0472
dc.identifier.otherWOS:000369602300007
dc.identifier.urihttp://dx.doi.org/10.1115/1.4032399
dc.identifier.urihttp://hdl.handle.net/10057/11954
dc.descriptionClick on the DOI link to access the article (may not be free).en_US
dc.description.abstractThe concept of engineering resilience has received a prevalent attention from academia as well as industry because it contributes a new means of thinking about how to withstand against disruptions and recover properly. Although the concept of resilience was scholarly explored in diverse disciplines, there are only few which focus on how to quantitatively measure the engineering resilience. This paper is dedicated to explore the gap between quantitative and qualitative assessment of engineering resilience in the domain of designing engineered systems in industrial applications. A conceptual framework is first proposed for modeling engineering resilience, and then Bayesian network (BN) is employed as a quantitative tool for the assessment and analysis of the resilience for engineered systems. Two industrial-based case studies, supply chain and production process, are employed to demonstrate the proposed approach. The proposed resilience quantification and analysis approach using BNs would empower system designers to have a better grasp of the weakness and strength of their own systems against system disruptions induced by adverse failure events.en_US
dc.description.sponsorshipNational Science Foundation under Grant No. CMMI-1351414 and Spirit AeroSystems under Grant No. PO-4400221590.en_US
dc.language.isoen_USen_US
dc.publisherASMEen_US
dc.relation.ispartofseriesJournal of Mechanical Design;v.138:no.3
dc.subjectEcosystem resilienceen_US
dc.subjectRisk-assessmenten_US
dc.subjectEnterpriseen_US
dc.subjectDisasteren_US
dc.subjectFrameworken_US
dc.titleResilience modeling and quantification for engineered systems using Bayesian networksen_US
dc.typeArticleen_US
dc.rights.holderCopyright © 2016 by ASMEen_US


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