Integrating digital twin technologies for maintenance 4.0 in the building industry: A review and conceptual framework
Hu Wei, Yeye Ou, Haiyi Liu, Peizhou Ni, Cheng Chang
Abstract
The building industry is facing increasing demands for sustainable and efficient maintenance practices, driven by advancements in Industry 4.0 technologies. Maintenance 4.0 emphasizes proactive maintenance strategies, including Condition-based Maintenance (CbM) and Predictive Maintenance (PdM), significantly enhanced by Digital Twin (DT) technology. DT enables the real-time monitoring, simulation, and optimization of building assets, offering substantial improvements in asset management, energy efficiency, and system longevity. However, integrating these technologies into the building industry's maintenance processes remains a challenge. This paper provides a comprehensive review of current research on DT-enabled Maintenance 4.0, presenting a conceptual framework that integrates enabling technologies and outlines their technological pipelines. It discusses the state-of-the-art methodologies, challenges, and future directions for the implementation of Maintenance 4.0 in the building sector, highlighting the potential of DT systems in optimizing maintenance strategies and enhancing decision-making. The study identifies key areas for further research, including data standardization, AI integration, and hybrid modeling approaches.