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.


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    Titel :

    Model Predictive Control for rail condition-based maintenance: A multilevel approach


    Beteiligte:
    Su, Zhou (Autor:in) / Nunez, Alfredo (Autor:in) / Baldi, Simone (Autor:in) / De Schutter, Bart (Autor:in)


    Erscheinungsdatum :

    01.11.2016


    Format / Umfang :

    172943 byte





    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



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