Chapter 10: Integrating digital twins with other digital technologies

This chapter introduces the integration of digital twin technologies with augmented reality (AR) and machine learning. Two real-life cases are presented to demonstrate that the operation and maintenance management process of building assets can be improved using the proposed AR-enhanced inspection system and QR code asset tags. In the first case, an AR-supported automated environmental anomaly detection is developed. The developed system focuses on the detection of environmental anomalies related to the thermal comfort of occupants within a building. Based on fault tree analysis (FTA), a decision-making tree is developed to assist facility managers in identifying corresponding failed assets according to the detected anomalous symptoms. The AR facilitates easy maintenance by highlighting failed assets hidden behind walls or ceilings onsite to the maintenance personnel. In the second case, a novel framework is proposed that exploits machine learning and advances in digital twin technology to prioritise maintenance actions in an automated and accurate way. To achieve this, asset users scan unique asset identifiers (tags), such as QR code asset tags, that link building elements to their ‘digital twin profiles’ through a mobile phone app. This allows users to not only see information about the assets, but also provide ‘comments’ describing issues and problems. The proposed framework features 99% accuracy, 99% precision, 98% recall and a 99% F₁ score in terms of inferring semantically important failure risk levels, based on which prioritisation is performed. Through these cases, the advancements in AR, machine learning and digital twins are brought together to support daily O&M management activities.