Hybrid Ai–Iot Framework For Predictive Maintenance In Critical Infrastructure: A Sustainable Approach To Reducing Energy Loss And System Failures

Abstract

Predictive maintenance has emerged as a critical strategy for enhancing operational reliability, reducing energy losses, and minimizing system failures across modern critical infrastructure. However, conventional monitoring systems often operate with limited real-time analytical capability, resulting in delayed failure detection and suboptimal maintenance decisions. This study proposes a hybrid Artificial Intelligence–Internet of Things (AI–IoT) framework designed to enable real-time condition monitoring, intelligent diagnostics, and energy-efficient maintenance scheduling. The framework integrates edge-based IoT sensor networks with cloud-driven machine learning algorithms—particularly deep learning and anomaly detection models—to capture high-frequency operational data while minimizing latency and computational overhead. A multi-layer architecture is developed, consisting of data acquisition, feature extraction, predictive modeling, and sustainability optimization modules. Experimental validation using a simulated critical infrastructure environment demonstrates that the proposed hybrid framework improves failure prediction accuracy by 18.7%, reduces energy loss by 12.5%, and decreases unplanned downtime by 22.3% compared to traditional maintenance approaches. These findings highlight the potential of the hybrid AI–IoT framework to support sustainable engineering practices, extend asset lifecycles, and enhance the resilience of essential infrastructure systems. The proposed model contributes novel insights into integrating smart sensing technologies with advanced computational intelligence for next-generation maintenance engineering.