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 languageEnglish
Title of host publicationProceedings of the IEEE 19th International Conference on Intelligent Transportation Systems (ITSC 2016)
EditorsR. Rosetti, D. Wolf
Place of PublicationPiscataway, NJ, USA
PublisherIEEE
Pages354-359
ISBN (Electronic)978-1-5090-1889-5
DOIs
Publication statusPublished - 2016
EventITSC 2016: 19th International Conference on Intelligent Transportation Systems - Rio de Janeiro, Brazil
Duration: 1 Nov 20164 Dec 2016
Conference number: 19

Conference

ConferenceITSC 2016: 19th International Conference on Intelligent Transportation Systems
Abbreviated titleITSC 2016
CountryBrazil
CityRio de Janeiro
Period1/11/164/12/16

    Research areas

  • Maintenance engineering, Rails, Degradation, Rail transportation, Planning, Uncertainty, Decision making

ID: 11341448