Reference:
Z. Su, A. Jamshidi, A. Núñez, S. Baldi, and B. De Schutter, "Distributed chance-constrained model predictive control for condition-based maintenance planning for railway infrastructures," in Predictive Maintenance in Dynamic Systems - Advanced Methods, Decision Support Tools and Real-World Applications (E. Lughofer and M. Sayed-Mouchaweh, eds.), Cham, Switzerland: Springer, ISBN 978-3-030-05644-5, pp. 533-554, 2019.Abstract:
We consider condition-based maintenance optimization for railway infrastructures, where the optimal planning of maintenance interventions is based on an explicit mathematical model describing the deterioration dynamics of the asset. We propose a novel model-based, optimization-based approach for condition-based maintenance planning of railway infrastructures. To make the proposed approach applicable to a wide range of defects in general railway infrastructures, we use a piecewise-affine model with bounded uncertain parameters as the deterioration model. The developed approach is robust but nonconservative, and the proposed distributed solution methods guarantee tractability even for large-scale infrastructure systems. We also present a case study that includes a comparison with two alternative maintenance planning approaches and that shows that the proposed chance-constrained maintenance planning approach is robust and cost-effective.Downloads:
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