Integration of Artificial Intelligence and Digital Twin Technologies for Predictive Maintenance and Lifecycle Optimization of Large-Scale Engineering Systems

Abstract

Large-scale engineering systems such as power plants, transportation networks, manufacturing facilities, and critical infrastructure assets are characterized by high operational complexity, long service life, and significant maintenance costs. Traditional maintenance strategies, including corrective and time-based preventive maintenance, are often inefficient and reactive, leading to unplanned downtime and suboptimal asset utilization. Recent advances in Artificial Intelligence and Digital Twin technologies offer transformative opportunities for predictive maintenance and lifecycle optimization of engineering systems. This paper investigates the integration of AI-driven analytics with digital twin frameworks to enable real- time monitoring, fault prediction, and decision support across the entire asset lifecycle. A comprehensive conceptual model is developed that combines sensor-driven data acquisition, virtual system representation, machine learning–based diagnostics, and prognostics. The study analyzes how data-driven digital twins enhance condition assessment, remaining useful life estimation, and maintenance scheduling. Implementation challenges related to data quality, model scalability, computational complexity, and cybersecurity are critically examined. The findings demonstrate that AI-enabled digital twins significantly improve maintenance accuracy, reduce lifecycle costs, and enhance system reliability. The paper concludes that the convergence of AI and digital twin technologies represents a foundational shift toward intelligent, self-adaptive engineering systems.