Abstract
Abstract As we all know, multi-level imperfect maintenance strategy is usually more effective than single-level maintenance strategy for the actual production machines. At the same time, in the previous multi-level maintenance strategies, the majority of maintenance models only consider the constant maintenance thresholds, while variable maintenance thresholds are usually ignored. Under these contexts, a novel multi-level imperfect maintenance model with variable preventive maintenance (PM) thresholds, variable overhaul maintenance (OM) thresholds and variable number of PMs in each OM cycle is established. In order to deal with the concerned problem, a novel twin delayed deep deterministic policy gradient (TD3) algorithm that is a kind of reinforcement learning is designed, renamed as NTD3. Finally, through numerical simulation, we can find that (1) the average improvements between the proposed maintenance strategy and other three traditional strategies in the average cost rate (ACR) are 11.50%, 595.91% and 5.16%, respectively; and (2) the average improvement between the proposed NTD3 and other random search method is 5.53%. Thus, the effectiveness of the proposed maintenance strategy and the superiority of proposed NTD3 are all demonstrated.
Published Version
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