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Maintenance strategy selection for multi-component systems using a combined analytic network process and cost-risk criticality model

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  • Mahmood Shafiee
  • Ashraf Labib
  • Jhareswar Maiti
  • Andrew Starr

Abstract

Selection of an appropriate maintenance strategy for multi-component systems is a very complex task due to diversity of components and their different failure modes, existence of various dependencies among components and a large number of competing criteria that need to be taken into consideration. This study presents a combined analytic network process and cost-risk criticality analysis model to select a cost-effective, low-risk maintenance strategy for different sets of components of a complex system. The proposed model consists of four maintenance alternatives (i.e. failure-based, time-based, risk-based and condition-based), among which the most appropriate strategy, on the basis of two criteria of maintenance implementation costs and failure criticality, is to be chosen. The former criterion includes the annual maintenance expenditure required for hardware, software and personnel training, while the latter criterion focuses on the capability of maintenance in reducing the failure vulnerability and enhancing the reliability and resilience. The possible dependencies among selection criteria as well as the failure interactions between components are taken into account to evaluate the maintenance alternatives. Finally, the model is applied to determine a suitable maintenance strategy for a new wind turbine configuration consisting of several mechanical, electrical and auxiliary components at the design stage. The results are compared with practices of maintenance over the first year of system operation as well as with the results obtained from an analytic hierarchy process model.

Suggested Citation

  • Mahmood Shafiee & Ashraf Labib & Jhareswar Maiti & Andrew Starr, 2019. "Maintenance strategy selection for multi-component systems using a combined analytic network process and cost-risk criticality model," Journal of Risk and Reliability, , vol. 233(2), pages 89-104, April.
    • Handle: RePEc:sae:risrel:v:233:y:2019:i:2:p:89-104
    DOI: 10.1177/1748006X17712071
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    References listed on IDEAS

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    Cited by:

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    4. Adedipe, Tosin & Shafiee, Mahmood & Zio, Enrico, 2020. "Bayesian Network Modelling for the Wind Energy Industry: An Overview," Reliability Engineering and System Safety, Elsevier, vol. 202(C).

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