AI-Driven Predictive Maintenance in Industrial IoT: A Review of Techniques and Applications

Abstract

Predictive maintenance has become a critical application of AI in Industrial Internet of Things (IIoT) environments, enabling early fault detection and reducing operational costs. This paper provides a detailed review of machine learning and deep learning techniques used for predictive maintenance, including time-series analysis, anomaly detection, and prognostics models. It examines data acquisition from IoT sensors, edge processing, and cloud integration. The study also discusses challenges such as data quality, real-time processing, and model generalization. Future directions include self-adaptive maintenance systems and integration with digital twin technologies.