Condition-based maintenance optimization by means of genetic algorithms and Monte Carlo simulation

Efficient maintenance policies are of fundamental importance in system engineering because of their fallbacks into the safety and economics of plants operation. When the condition of a system, such as its degradation level, can be continuously monitored, a Condition-Based Maintenance (CBM) policy can be implemented, according to which the decision of maintaining the system is taken dynamically on the basis of the observed condition of the system. In this paper, we consider a continuously monitored multi-component system and use a Genetic Algorithm (GA) for determining the optimal degradation level beyond which preventive maintenance has to be performed. The problem is framed as a multi-objective search aiming at simultaneously optimizing two typical objectives of interest, profit and availability. For a closer adherence to reality, the predictive model describing the evolution of the degrading system is based on the use of Monte Carlo (MC) simulation. More precisely, the flexibility offered by the simulation scheme is exploited to model the dynamics of a stress-dependent degradation process in load-sharing components and to account for limitations in the number of maintenance technicians available. The coupled (GA[plus ]MC) approach is rendered particularly efficient by the use of the ‘drop-by-drop’ technique, previously introduced by some of the authors, which allows to effectively drive the combinatorial search towards the most promising solutions.