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Please use this identifier to cite or link to this item: https://dspace.lboro.ac.uk/2134/20737

Title: A model-based approach to system of systems risk management
Authors: Kinder, Andrew M.K.
Henshaw, Michael
Siemieniuch, Carys E.
Keywords: Systems of systems
Risk management modelling
Issue Date: 2015
Publisher: © IEEE
Citation: KINDER, A., HENSHAW, M. and SIMIENIUCH, C.E., 2015. A model-based approach to system of systems risk management. IN: Proceedings of 2015 10th IEEE System of Systems Engineering Conference (SoSE), San Antonio, United States, 17-20 May 2015, pp.122-127.
Abstract: This paper discusses the approaches required for risk management of ‘traditional’ (single) Systems and System of Systems (SoS) and identifies key differences between them. When engineering systems, the Risk Management methods applied tend to use qualitative techniques, which provide subjective probabilities and it is argued that, due to the inherent complexity of SoS, more quantitative methods must be adopted. The management of SoS risk must be holistic and should not assume that if risks are managed at the system level then SoS risk will be managed implicitly. A model-based approach is outlined, utilizing a central Bayesian Belief Network (BBN) to represent risks and contributing factors. Supporting models are run using a Monte Carlo approach, thereby generating results, which may be ‘learnt’ by the BBN, reducing the reliance on subjective data.
Description: THIS DOCUMENT IS CLOSED ACCESS. © 2015 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
Version: Published
DOI: 10.1109/SYSOSE.2015.7151940
URI: https://dspace.lboro.ac.uk/2134/20737
Publisher Link: http://dx.doi.org/10.1109/SYSOSE.2015.7151940
Appears in Collections:Closed Access (Mechanical, Electrical and Manufacturing Engineering)

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