Digital twin-enabled anomaly detection for built asset monitoring in operation and maintenance
By Q. Lu, Xiang Xie, A. Parlikad, J. Schooling. Automation in Construction, 2020
395 citations recorded by the source corpus.
Abstract Effective asset management plays a significant role in delivering the functionality and serviceability of buildings. However, there is a lack of efficient strategies and comprehensive approaches for managing assets and their associated data that can help to monitor, detect, record, and communicate operation and maintenance (O&M) issues. With the importance of Digital Twin (DT) concepts being proven in the architecture, engineering, construction and facility management (AEC/FM) sectors, a DT-enabled anomaly detection system for asset monitoring and its data integration method based on extended industry foundation classes (IFC) in daily O&M management are provided in this study. This paper presents a novel IFC-based data structure, using which a set of monitoring data that carries diagnostic information on the operational condition of assets is extracted from building DTs. Considering that assets run under changing loads determined by human demands, a Bayesian change point detection methodology that handles the contextual features of operational data is adopted to identify and filter contextual anomalies through cross-referencing with external operation information. Using the centrifugal pumps in the heating, ventilation and air-cooling (HVAC) system as a case study, the results indicate and prove that the novel DT-based anomaly detection process flow realizes a continuous anomaly detection of pumps, which contributes to efficient and automated asset monitoring in O&M.
Subfields: Building and Construction, Industrial and Manufacturing Engineering, Medical Laboratory Technology
View Paper: https://www.semanticscholar.org/paper/851cd26d91c4f2848289a0e9749e912800843fbc
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Building and Construction
- Carol C. Menassa
- Vineet R. Kamat
- Victor C. Li
- Mingyan Deng
- M. Şahmaran
- R. Ranade
- Christopher K. Y. Leung
- E. Yang
In Industrial and Manufacturing Engineering
Citations in the corpus
- Integrated digital twin and blockchain framework to support accountable information sharing in construction projects (2021, background): “Previously, Lu et al. [22] integrated the digital twin with industry foundation classes (IFC) and daily operation and management data for anomaly detection and operation and maintenance (O & M) management in an HVAC system.”
- Digital Twins: A Survey on Enabling Technologies, Challenges, Trends and Future Prospects (2022, methodology): “…of training dataset [160] and anomalous dataset [88] for better model training, compatibility with heterogeneous data and its pragmatic integration [127], real-time analytics [162], [163] and generation, preservation of signature copies of every entity in extensive sensor networks [159] to resolve…”
- From BIM to digital twins: a systematic review of the evolution of intelligent building representations in the AEC-FM industry (2021, positive): “The basis of the integration of BIM and IoT has led to the emergence of the Digital Twin (Lu et al. 2020, Tagliabue et al. 2021).”