General-purpose maintenance planning using deep reinforcement learning and Monte Carlo tree search
Sammanfattning: Maintenance planning and execution is increasingly important for the modern industrial sector. Maintenance costs can amount to a major part of industrial spending. However, it is not as simple as just reducing maintenance budgets. A balance must be struck between risking unplanned downtime and the costs of maintenance efforts, in order to keep the profit margins needed to compete in the global markets of today. One approach to improve the effectiveness of industries is to apply intelligent maintenance planners. In this thesis, a general-purpose maintenance planner based on Monte-Carlotree search and deep reinforcement learning is presented. This planner was evaluated and compared against two different periodic planners as well as the oracle lower bound on four different maintenance scenarios. These four scenarios are all based on servicing wind turbines. All scenarios include imperfect maintenance actions, as well as uncertainty in terms of the outcomes of maintenance actions. Furthermore, the four scenarios include both single and multi-component variants. The evaluation showed that the proposed method is outperforming both periodic planners in three of the four scenarios, with the forth being inconclusive. These results indicate that the maintenance planner introduced in this paper is a viable method, at least for these types of maintenance problems. However, further research is needed on this topic of maintenance planning under uncertainty. More specifically, the viability of the proposed method on a more diverse set of maintenance problems is needed to draw any clear general conclusions. Finally, possible improvements to the training process that are discussed in this thesis should be investigated.
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