Xie, X;
Lu, Q;
Parlikad, AK;
Schooling, JM;
(2020)
Digital Twin Enabled Asset Anomaly Detection for Building Facility Management.
In:
IFAC-PapersOnLine.
(pp. pp. 380-385).
Elsevier
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Abstract
Assets play a significant role in building utilities by undertaking the majority of their service functionalities. However, a comprehensive facility management solution that can help to monitor, detect, record and communicate asset anomalous issues is till nowhere to be found. The digital twin concept is gaining increasing popularity in architecture, engineering and construction/facility management (AEC/FM) sector, and a digital twin enabled asset condition monitoring and anomaly detection framework is proposed in this paper. A Bayesian change point detection methodology is tentatively embedded to reveal the suspicious asset anomalies in a real time manner. A demonstrator on cooling pumps is developed and implemented based on Centre for Digital Built Britain (CDBB) West Cambridge Digital Twin Pilot. The results demonstrate that supported by the data management capability provided by digital twin, the proposed framework realizes a continuous condition monitoring and anomaly detection for single asset, which contributes to efficient and automated asset monitoring in O&M management.
| Type: | Proceedings paper |
|---|---|
| Title: | Digital Twin Enabled Asset Anomaly Detection for Building Facility Management |
| Event: | 4th International-Federation-of-Automatic-Control (IFAC) Workshop on Advanced Maintenance Engineering, Services and Technologies (AMEST) |
| Location: | Cambridge, ENGLAND |
| Dates: | 10 September 2020 - 11 September 2020 |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.1016/j.ifacol.2020.11.061 |
| Publisher version: | https://doi.org/10.1016/j.ifacol.2020.11.061 |
| Language: | English |
| Additional information: | Copyright © 2020 The Authors. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0) |
| Keywords: | Building Information Modeling, Digital Twin, Facility Management, Asset Management, Condition Monitoring, Anomaly Detection |
| 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/10136502 |
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