Impact of Digital Twin Technology on Predictive Maintenance and Asset Lifecycle Management in Energy Infrastructure: A Quantitative Evaluation

Abstract

This study examined the impact of digital twin technology on predictive maintenance and asset lifecycle management within energy infrastructure through a comprehensive quantitative evaluation. A quasi-experimental, comparative, and longitudinal research design was adopted to analyze operational data collected from 180 energy assets, including power generation units, substations, and grid-connected systems. The study compared traditional maintenance strategies with digital twin-enabled predictive maintenance using key performance indicators such as downtime, failure frequency, maintenance cost, repair duration, and asset availability. Descriptive statistics, analysis of variance, and multivariate regression techniques were applied to assess performance differences and relationships among variables. The findings demonstrated that digital twin-enabled systems significantly outperformed traditional approaches, with average downtime reduced from 18.4 hours to 9.7 hours per month, representing a 47.3% improvement. Asset availability increased from 82.3% to 91.8%, while mean time between failures improved from 320 hours to 510 hours, indicating enhanced reliability. Maintenance costs decreased from an average of USD 4,950 to USD 3,420 per month, reflecting a reduction of approximately 30.9%. Statistical analysis confirmed that these differences were significant at the 0.05 level, with strong effect sizes observed across multiple indicators. Regression results further indicated that digital twin functionalities, including real-time monitoring and predictive alert accuracy, accounted for 68% of the variance in maintenance performance outcomes. Subgroup analysis revealed that high-criticality and older assets experienced the greatest benefits, with downtime reductions exceeding 50% in optimized environments. Overall, the study provided robust empirical evidence that digital twin technology enhances predictive maintenance effectiveness, improves operational reliability, and supports more efficient asset lifecycle management in energy infrastructure systems.