Lu, Q;
Xie, X;
Parlikad, AK;
Schooling, JM;
(2020)
Digital twin-enabled anomaly detection for built asset monitoring in operation and maintenance.
Automation in Construction
, 118
, Article 103277. 10.1016/j.autcon.2020.103277.
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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.
| Type: | Article |
|---|---|
| Title: | Digital twin-enabled anomaly detection for built asset monitoring in operation and maintenance |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.1016/j.autcon.2020.103277 |
| Publisher version: | https://doi.org/10.1016/j.autcon.2020.103277 |
| Language: | English |
| Additional information: | This version is the author accepted manuscript. For information on re-use, please refer to the publisher’s terms and conditions. |
| Keywords: | Digital twin, Anomaly detection, Industry Foundation Classes (IFC), Operation and Maintenance management |
| UCL classification: | UCL UCL > Provost and Vice Provost Offices > UCL BEAMS UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of the Built Environment |
| URI: | https://discovery.ucl.ac.uk/id/eprint/10102713 |
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