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Optimal Maintenance of Naval Vessels Considering Service Life Uncertainty

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Model Validation and Uncertainty Quantification, Volume 3

Abstract

Life-cycle analysis is increasingly being used to assess the structural performance and total cost incurred during the service life of naval vessels. Decision making on maintenance scheduling is based on minimizing life-cycle cost and risk metrics. However, existing life-cycle frameworks generally consider deterministic service life in the analysis process. Due to budgetary reasons, naval vessels are often required to extend their service life beyond the originally designed life. Management plans based on deterministic service life concepts do not consider the uncertainties associated with service life assessment and prediction. Structural aging can have significant effects on operational costs and risks during the service life extension. This paper presents the modeling of service life uncertainty and its impacts on the life-cycle management of naval vessels. The proposed framework is aimed to provide robust maintenance strategies addressing service life uncertainties. The presented approach is applied to fatigue of a high speed naval vessel.

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Acknowledgements

The support from the U.S. Office of Naval Research (contracts N00014-08-1-0188, N00014-12-1-0023, and N00014-16-1-2299, Structural Reliability Program), the NSF Award CMMI-1537926, and the Commonwealth of Pennsylvania, Department of Community and Economic Development, through the Pennsylvania Infrastructure Technology Alliance (PITA) is gratefully acknowledged. The opinions and conclusions presented in this paper are those of the authors and do not necessarily reflect the views of the sponsoring organizations.

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Liu, Y., Frangopol, D.M. (2019). Optimal Maintenance of Naval Vessels Considering Service Life Uncertainty. In: Barthorpe, R. (eds) Model Validation and Uncertainty Quantification, Volume 3. Conference Proceedings of the Society for Experimental Mechanics Series. Springer, Cham. https://doi.org/10.1007/978-3-319-74793-4_35

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  • DOI: https://doi.org/10.1007/978-3-319-74793-4_35

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-74792-7

  • Online ISBN: 978-3-319-74793-4

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