Maintenance optimization in complex systems using prognostic information

International audienceIn this research, we propose effective optimization approaches for multi-component complex systems to support preventive maintenance decision-making. The considered complex system consists of several operational stages, and each stage contains multiple redundant components. To implement predictive maintenance, we use component-level Remaining Useful Life information to achieve system-level availability in generic complex systems. The aim of this work is to coordinate component redundancy and maintenance in different stages to keep the main industry producing continuously such that client demands are satisfied as much as possible in the planning horizon. For the problem, we formulate a mixed-integer linear programming model to minimize the overall cost. Simulation results demonstrate the effectiveness of the proposed approach for providing maintenance decision support