Predictive Maintenance and Intelligent Manufacturing befitting Industry 4.0

Abstract

Manufacturing systems operating under Industry 4.0 principles increasingly rely on data-driven intelligence to improve reliability, efficiency, and operational continuity. Predictive maintenance plays a critical role in this transformation by enabling maintenance decisions to be informed by equipment behavior rather than fixed schedules. This work investigates how learning based predictive maintenance can be integrated into intelligent manufacturing environments to support proactive intervention and stable production performance. A system architecture is examined that combines industrial sensing, analytics, and execution-level integration to generate actionable maintenance insights. Experimental evaluation demonstrates improvements in failure anticipation, maintenance efficiency, and production stability under realistic operating conditions. The findings indicate that predictive maintenance contributes not only to reduced downtime but also to more resilient and adaptive manufacturing operations.