This paper presents an innovative approach that involves the optimization of maintenance for production equipment by leveraging advanced artificial intelligence (AI) algorithms. The study explores the application of Markov process theory within the context of reinforcement learning and its integration into the modeling of production environments. The focus of this research is to address critical issues related to enhancing the reliability and efficiency of production processes. This is achieved by reducing maintenance costs and minimizing equipment downtime. The proposed model for optimizing the use of the production equipment system is described as an environment with discrete states, where an agent operates with the capability to perform specific actions. This model emphasizes the implementation of the Q-learning algorithm, a form of reinforcement learning that aims to optimize production processes by enabling the agent to learn and make decisions that enhance system performance. Through the iterative process of Q-learning, the agent evaluates the potential benefits of various actions in different states, gradually refining its strategy to maximize long-term rewards. Q-learning, with its ability to handle environments with unknown dynamics, is particularly effective in this context. It helps the agent to develop an optimal maintenance policy by balancing immediate maintenance costs against the long-term benefits of reduced downtime and extended equipment lifespan. The iterative nature of Q-learning ensures continuous improvement and adaptation, making the system robust against varying operational conditions and unforeseen disruptions. Through rigorous analysis and experimentation, the findings demonstrate a significant improvement in the reliability and productivity of the production equipment system. The introduction of AI technologies, specifically through the Q-learning framework, not only streamlines maintenance practices but also ensures a more efficient utilization of resources, ultimately leading to a more robust and cost-effective production environment. The results of this study highlight the potential of AI in transforming traditional production maintenance strategies and setting new standards for operational excellence.

productionsystem, productionequipment, artificialintelligence, Markovprocesses, reliabilityoffunctioning, efficiencyofuse

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