The DOI matches its Crossref record; 45 of the 45 references it cites were found in a registry or an index.
Digital twin-enabled anomaly detection for built asset monitoring in operation and maintenance
Open accessAbstract
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.
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Developing a Digital Twin at Building and City Levels: Case Study of West Cambridge Campus
Lu, Q., Parlikad, A. K., Woodall, P., Don Ranasinghe, G., Xie, X., Liang, Z., Konstantinou, E., Heaton, J., & Schooling, J. (2020) · Journal of Management in Engineering · doi:10.1061/(asce)me.1943-5479.0000763 · Cited by 531
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Cited by
Digital Twins: A Survey on Enabling Technologies, Challenges, Trends and Future Prospects
Mihai, S., Yaqoob, M., Hung, D. V., Davis, W., Towakel, P., Raza, M., Karamanoglu, M., Barn, B., Shetve, D., Prasad, R. V., et al. (2022) · IEEE Communications Surveys & Tutorials · doi:10.1109/comst.2022.3208773 · Cited by 1,049
Digital twin application in the construction industry: A literature review
Opoku, D.-G. J., Perera, S., Osei-Kyei, R., & Rashidi, M. (2021) · Journal of Building Engineering · doi:10.1016/j.jobe.2021.102726 · Cited by 753
A BIM-data mining integrated digital twin framework for advanced project management
Pan, Y., & Zhang, L. (2021) · Automation in Construction · doi:10.1016/j.autcon.2021.103564 · Cited by 518
A comprehensive review of digital twin — part 1: modeling and twinning enabling technologies
Thelen, A., Zhang, X., Fink, O., Lu, Y., Ghosh, S., Youn, B. D., Todd, M. D., Mahadevan, S., Hu, C., & Hu, Z. (2022) · Structural and Multidisciplinary Optimization · doi:10.1007/s00158-022-03425-4 · Cited by 441
Lee, D., Lee, S. H., Masoud, N., Krishnan, M. S., & Li, V. C. (2021) · Automation in Construction · doi:10.1016/j.autcon.2021.103688 · Cited by 428
Metadata
- Authors
- Lu, Q., Xie, X., Parlikad, A. K., & Schooling, J. M.
- Venue
- Automation in Construction
- Publisher
- Elsevier BV
- Published
- 2020-10
- Volume
- 118
- Pages
- 103277
- ISSN
- 0926-5805 (ISSN-L)
- OpenAlex
- W3028926798
- Cited by
- 481
- References
- 45
- FWCI
- 24.99
- Type
- journal article
- Language
- en
- Open access
- green
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