Artificial Intelligence-Based Predictive Maintenance for Industrial Manufacturing Systems

Abstract

The integration of artificial intelligence (AI) into industrial maintenance has created new opportunities for improving equipment reliability and reducing unexpected production losses. This study examines an AI-based predictive maintenance framework for industrial manufacturing systems using machine-learning techniques to analyze equipment condition and operational data. The proposed approach considers parameters such as temperature, vibration, pressure, energy consumption and operating time to identify potential equipment failures before they occur. The framework can support continuous condition monitoring, early fault detection and maintenance scheduling. The study further discusses the potential of predictive analytics to reduce unplanned downtime, maintenance costs and production interruptions while improving equipment utilization. The findings indicate that combining industrial sensors with machine-learning algorithms can provide an effective foundation for intelligent maintenance systems. The proposed framework demonstrates the potential of AI-driven maintenance for developing more reliable, efficient and digitally connected manufacturing environments.