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Digital Twin Enabled Asset Anomaly Detection for Building Facility Management

Lookup NU author(s): Dr Xiang XieORCiD

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This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License (CC BY-NC-ND).

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.

Publication metadata

Author(s): Xie X, Lu QC, Parlikad A, Schooling J

Publication type: Conference Proceedings (inc. Abstract)

Publication status: Published

Conference Name: 4th IFAC Workshop on Advanced Maintenance Engineering, Services and Technologies

Year of Conference: 2020

Pages: 380-385

Online publication date: 18/12/2020

Acceptance date: 02/04/2020

Date deposited: 30/11/2022

ISSN: 2405-8963

Publisher: Elsevier Ltd.

URL: https://doi.org/10.1016/j.ifacol.2020.11.061

DOI: 10.1016/j.ifacol.2020.11.061

Series Title: IFAC-PapersOnLine

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