Highlights Machine learning methods on easy-to-obtain data without loss of accuracy. A risk-averse and hybrid prediction model to control the underprediction of defects. Integration of prediction and scheduling via MDPs for unconstrained problems. Integration of prediction and scheduling via Restless Bandits for constrained cases. All models are derived and validated with real data.
Abstract Inspection and maintenance activities are essential to preserving safety and cost-effectiveness in railways. However, the stochastic nature of railway defect occurrence is usually ignored in literature; instead, defect stochasticity is considered independently of maintenance scheduling. This study presents a new approach to predict rail and geometry defects that relies on easy-to-obtain data and integrates prediction with inspection and maintenance scheduling activities. In the proposed approach, a novel use of risk-averse and hybrid prediction methodology controls the underestimation of defects. Then, a discounted Markov decision process model utilizes these predictions to determine optimal inspection and maintenance scheduling policies. Furthermore, in the presence of capacity constraints, Whittle indices via the multi-armed restless bandit formulation dynamically provide the optimal policies using the updated transition kernels. Results indicate a high accuracy rate in prediction and effective long-term scheduling policies that are adaptable to changing conditions.
Data-driven predictive maintenance scheduling policies for railways
Tramsportation Research, Part C: Emerging Technologies ; 107 ; 137-154
2019-07-23
18 pages
Aufsatz (Zeitschrift)
Elektronische Ressource
Englisch
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