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.
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BibTeX
@incollection{SuJam:19-023,
author = {Su, Zhou and Jamshidi, Ali and N{\'{u}}{\~{n}}ez, Alfredo and
Baldi, Simone and De Schutter, Bart},
title = {Distributed Chance-Constrained Model Predictive Control for
Condition-Based Maintenance Planning for Railway
Infrastructures},
booktitle = {Predictive Maintenance in Dynamic Systems -- Advanced Methods,
Decision Support Tools and Real-World Applications},
editor = {Lughofer, Edwin and Sayed-Mouchaweh, Moamar},
publisher = {Springer},
address = {Cham, Switzerland},
pages = {533--554},
year = {2019}
}