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A dynamic unreliability assessment and optimal maintenance strategies for multistate weighted k-out-of-n:F systems

In this paper, a dynamic evaluation of the multistate weighted k‐out‐of‐n:F system is presented in an unreliability viewpoint. The expected failure cost of components is used as an unreliability index. Using failure cost provides an opportunity to employ financial concepts in system unreliability es...

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Published in:Applied stochastic models in business and industry 2016-07, Vol.32 (4), p.485-493
Main Authors: Khorshidi, Hadi Akbarzade, Gunawan, Indra, Ibrahim, Yousef
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Language:English
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description In this paper, a dynamic evaluation of the multistate weighted k‐out‐of‐n:F system is presented in an unreliability viewpoint. The expected failure cost of components is used as an unreliability index. Using failure cost provides an opportunity to employ financial concepts in system unreliability estimation. Hence, system unreliability and system cost can be compared easily in order to making decision. The components' probabilities are computed over time to model the dynamic behavior of the system. The whole system has been assessed by recursive algorithm approach. As a result, a bi‐objective optimization model can be developed to find optimal decisions on maintenance strategies. Finally, the application of the proposed model is investigated via a transportation system case. Matlab programming is developed for the case, and genetic algorithm is used to solve the optimization model. Copyright © 2016 John Wiley & Sons, Ltd.
doi_str_mv 10.1002/asmb.2173
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ispartof Applied stochastic models in business and industry, 2016-07, Vol.32 (4), p.485-493
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subjects bi-objective optimization
dynamic assessment
Dynamic tests
Dynamical systems
Dynamics
Failure
failure cost
Maintenance
Matlab
multistate weighted k-out-of-n:F system
Optimization
recursive algorithm
Transportation models
unreliability evaluation
title A dynamic unreliability assessment and optimal maintenance strategies for multistate weighted k-out-of-n:F systems
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