AI-Enabled Predictive Maintenance Framework for Industrial Sensor Data and Operational Analytics - Elena M. Petrova (Kalam Practica Media, 2026)
Industrial systems increasingly rely on sensor networks, cyber-physical infrastructure, and operational analytics to maintain equipment reliability under complex production conditions. Predictive maintenance has become a central Industry 4.0 strategy because artificial intelligence can detect degradation patterns, forecast failures, and support maintenance decisions before breakdowns occur. This study developed an integrated literature-based framework for applying artificial intelligence to predictive maintenance in industrial systems using sensor data and operational analytics. A structured literature review was conducted on 45 academic sources published between 2015 and 2025. The review coded studies according to data source, algorithmic approach, industrial application, prediction task, evaluation metric, deployment barrier, and decision-support function. The synthesis produced a multi-layer framework linking sensor acquisition, data preprocessing, feature engineering, model development, remaining useful life estimation, anomaly detection, maintenance decision analytics, and feedback learning. Machine learning appeared in 73.33% of reviewed studies, deep learning in 57.78%, remaining useful life prediction in 62.22%, anomaly detection in 48.89%, and hybrid or physics-informed approaches in 28.89%. The proposed framework identifies five critical implementation layers: sensing infrastructure, data engineering, artificial intelligence modeling, operational decision support, and continuous reliability feedback. Artificial intelligence strengthens predictive maintenance when it is embedded into an industrial analytics pipeline rather than treated as an isolated prediction model. The proposed framework can support industrial managers, engineers, and maintenance planners in designing scalable and trustworthy predictive maintenance systems.