Abstract
Condition-based maintenance (CBM) involves making decisions on maintenance or repair based on the actual deterioration conditions of the components. The long-run average cost is minimized by choosing the right maintenance action at the right time. In this study, considering the uncertainty of health status cognition and the limitation of detection information, the digital twin is introduced into the maintenance decision method to realize the intelligent operation and maintenance of mechanical equipment and parts by simulating the CBM decision-making problem as a continuous semi-Markov decision process (CSMDP). For tool wear, the application of a reinforcement learning (RL) algorithm based on the digital twin in CBM is described. The convergence of the digital twin and RL algorithm is used to learn the optimal maintenance decisions and inspection schedule based on the current health state of the component.