Visualised inspection system for monitoring environmental anomalies during daily operation and maintenance
Engineering, Construction and Architectural Management
ISSN: 0969-9988
Article publication date: 7 July 2020
Issue publication date: 21 September 2020
Abstract
Purpose
Visual inspection and human judgement form the cornerstone of daily operations and maintenance (O&M) services activities carried out by facility managers nowadays. Recent advances in technologies such as building information modelling (BIM), distributed sensor networks, augmented reality (AR) technologies and digital twins present an immense opportunity to radically improve the way daily O&M is conducted. This paper aims to describe the development of an AR-supported automated environmental anomaly detection and fault isolation method to assist facility managers in addressing problems that affect building occupants’ thermal comfort.
Design/methodology/approach
The developed system focusses on the detection of environmental anomalies related to the thermal comfort of occupants within a building. The performance of three anomaly detection algorithms in terms of their ability to detect indoor temperature anomalies is compared. Based on the fault tree analysis (FTA), a decision-making tree is developed to assist facility management (FM) professionals in identifying corresponding failed assets according to the detected anomalous symptoms. The AR system facilitates easy maintenance by highlighting the failed assets hidden behind walls/ceilings on site to the maintenance personnel. The system can thus provide enhanced support to facility managers in their daily O&M activities such as inspection, recording, communication and verification.
Findings
Taking the indoor temperature inspection as an example, the case study demonstrates that the O&M management process can be improved using the proposed AR-enhanced inspection system. Comparative analysis of different anomaly detection algorithms reveals that the binary segmentation-based change point detection is effective and efficient in identifying temperature anomalies. The decision-making tree supported by FTA helps formalise the linkage between temperature issues and the corresponding failed assets. Finally, the AR-based model enhanced the maintenance process by visualising and highlighting the hidden failed assets to the maintenance personnel on site.
Originality/value
The originality lies in bringing together the advances in augmented reality, digital twins and data-driven decision-making to support the daily O&M management activities. In particular, the paper presents a novel binary segmentation-based change point detection for identifying temperature anomalous symptoms, a decision-making tree for matching the symptoms to the failed assets, and an AR system for visualising those assets with related information.
Keywords
Acknowledgements
This research that contributed to this paper was supported by the Centre for Digital Built Britain (CDBB) with funding provided through the Government’s modern industrial strategy by Innovate UK, part of UK Research and Innovation. It was also partly funded by the EPSRC/Innovate UK Centre for Smart Infrastructure and Construction (Grant Numbers EP/N021614/1 and 920035). This paper is based on a conference paper presented by the authors at the ASCE Construction Research Congress (CRC) 2020.
Citation
Xie, X., Lu, Q., Rodenas-Herraiz, D., Parlikad, A.K. and Schooling, J.M. (2020), "Visualised inspection system for monitoring environmental anomalies during daily operation and maintenance", Engineering, Construction and Architectural Management, Vol. 27 No. 8, pp. 1835-1852. https://doi.org/10.1108/ECAM-11-2019-0640
Publisher
:Emerald Publishing Limited
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