Abstract
This paper develops a multilevel decision making approach based on model predictive control (MPC) for condition-based maintenance of rail. We address a typical railway surface defect called “squat”, in which three maintenance actions can be considered: no maintenance, grinding, and replacement. A scenario-based scheme is applied to address the uncertainty in the deterioration dynamics of the key performance indicator for each track section, and a piecewise-affine model is used to approximate the expected dynamics, which is to be optimized by a scenario-based MPC controller at the high level. A static optimization problem involving clustering and mixed integer linear programming is solved at the low level to produce an efficient grinding and replacing schedule. A case study using real measurements obtained from a Dutch railway line between Eindhoven and Weert is performed to demonstrate the merits of the proposed approach.
Original language | English |
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Title of host publication | Proceedings of the IEEE 19th International Conference on Intelligent Transportation Systems (ITSC 2016) |
Editors | R. Rosetti, D. Wolf |
Place of Publication | Piscataway, NJ, USA |
Publisher | IEEE |
Pages | 354-359 |
ISBN (Electronic) | 978-1-5090-1889-5 |
DOIs | |
Publication status | Published - 2016 |
Event | ITSC 2016: 19th International Conference on Intelligent Transportation Systems - Rio de Janeiro, Brazil Duration: 1 Nov 2016 → 4 Dec 2016 Conference number: 19 |
Conference
Conference | ITSC 2016: 19th International Conference on Intelligent Transportation Systems |
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Abbreviated title | ITSC 2016 |
Country/Territory | Brazil |
City | Rio de Janeiro |
Period | 1/11/16 → 4/12/16 |
Keywords
- Maintenance engineering
- Rails
- Degradation
- Rail transportation
- Planning
- Uncertainty
- Decision making