AI-Driven Predictive Maintenance for Industrial IoT Systems

Abstract

This paper investigates the application of AI in predictive maintenance for Industrial Internet of Things (IIoT) systems. A machine learning pipeline is proposed, integrating time-series analysis and anomaly detection to predict equipment failures. The model is validated on real-world IIoT datasets, achieving a predictive accuracy of 93.2% and reducing unplanned downtime by 30%. The findings demonstrate the potential of AI to enhance operational efficiency and reduce maintenance costs.