Abstract

Industrial Internet of Things (IIoT) systems generate vast amounts of sensor and operational data, enabling a shift from reactive or scheduled maintenance to predictive maintenance (PdM). Predictive maintenance forecasts equipment failures before they occur, reducing unplanned downtime, extending asset lifespan, and optimizing maintenance costs. Traditional maintenance strategies often rely on pre‑defined thresholds or time‑based interventions, which lack responsiveness to real workload conditions. Integration of Artificial Intelligence (AI) with IIoT combines real‑time condition monitoring, machine learning, and advanced analytics to create intelligent maintenance frameworks that learn from data patterns and predict faults with high accuracy. This paper investigates AI‑driven predictive maintenance frameworks tailored to IIoT environments, including architectural components such as sensor networks, data preprocessing, feature extraction, model training, and deployment in cloud or edge infrastructures. Emphasis is placed on machine learning models (e.g., random forests, neural networks, support vector machines) and deep learning architectures (e.g., LSTM, CNN) employed for anomaly detection and Remaining Useful Life (RUL) estimation. The study also explores practical challenges, including data quality, model interpretability, cybersecurity concerns, and scalability. Finally, future directions are discussed, advocating for explainable AI, digital twin integration, and hybrid learning models to further enhance reliability and industrial adoption. (ResearchGate)