AI-Driven Predictive Maintenance In IoT-Enabled Conveyor Belt Systems

Abstract

Conveyor belt systems are critical in industrial operations, yet traditional maintenance approaches often lead to unplanned downtime, high operational costs, and reduced equipment lifespan. This paper presents an AI-driven predictive maintenance framework for IoT-enabled conveyor belt systems. IoT sensors continuously monitor operational parameters such as vibration, temperature, motor current, and belt speed, generating real-time data for analysis. Machine learning and deep learning algorithms process this data to detect anomalies, predict potential failures, and estimate the remaining useful life of critical components. The proposed framework enables proactive maintenance, minimizes downtime and maintenance costs, and enhances overall system reliability and safety. A user-friendly dashboard provides real-time alerts and actionable recommendations, facilitating timely maintenance decisions. Implementing AI-based predictive maintenance in IoT-enabled conveyor systems aligns with Industry 4.0 objectives, supporting smarter, more efficient, and reliable industrial operations.