OPTIMIZING PREDICTIVE MAINTENANCE IN SMART MANUFACTURING USING DEEP REINFORCEMENT LEARNING-DRIVEN INDUSTRIAL AI SYSTEMS

Abstract

Preventative maintenance has become a vital tool in the age of Industry 4.0, helping to maximize machine efficiency and reduce downtime in smart industrial settings. In this research, deep reinforcement learning (DRL) coupled with industrial artificial intelligence (AI) systems is used to propose an innovative method for preventative maintenance. Through the utilization of DRL's capacity to acquire suitable servicing policies via communication with machine information in real-time, the suggested system adjusts itself dynamically to changing operating circumstances. The creation of a multi-agent DRL framework, which reduces unplanned downtime and boosts operational effectiveness by predicting equipment breakdowns and scheduling maintenance tasks in real time, is one of the key achievements. In addition, based on artificial intelligence, analytics are used to improve the algorithm to evaluate machine wellness, allocate resources optimally, and prolong equipment lifespan. The suggested solution outperforms conventional maintenance techniques in terms of precision and flexibility, opening the door for more effective, self-optimizing manufacturing techniques. Experiments conducted in a smart manufacturing environment demonstrate notable gains being manufacturing continuity, decision-making effectiveness, and operational reduction in costs.