Digital Twin for Fault Detection and Diagnosis of Building Operations: A Systematic Review

Intelligence in Industry 4.0 has led to the development of smart buildings with various control systems for data collection, efficient optimization, and fault detection diagnosis (FDD). However, buildings, especially regard heating, ventilation, air conditioning (HVAC) systems, are responsible significant global energy consumption. Digital Twin (DT) technology offers a sustainable solution facility management. This study comprehensively reviews DT performance evaluation building life cycle predictive maintenance. 200 relevant papers were selected using systematic methodology from Scopus, Web Science, Google Scholar, FDD methods reviewed identify their advantages limitations. In conclusion, data-driven gaining popularity due ability handle large amounts improve accuracy, flexibility, adaptability. Unsupervised semi-supervised learning as important operations, such HVAC they can unlabeled complex patterns anomalies. Future studies should focus on developing interpretable models understand how made predictions. Hybrid that combine different approaches show promise reliable further research. Additionally, deep analyze datasets, indicating promising area investigation.