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, 27(8), pp. 1835-1852. ISSN 0969-9988
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.
| Item Type: | Article |
|---|---|
| Uncontrolled Keywords: | anomaly detection; augmented reality; digital twin; operations and maintenance |
| Index terms: | digital twin, data-driven decision-making, methodology, decision-making, fault-tree analysis, augmented reality, comparative analysis, visual inspection, manager, personnel, indoor temperature, operation and maintenance, monitoring, thermal comfort, linkage, inspection, building information modelling, case study |
| Subjects: | information systems, quality assurance, environmental engineering, decision analysis, management, maintenance engineering, research methods, professional practice, data analysis and analytics, environmental science, visualization, risk management, practitioner, control systems, data collection methods, business, digital engineering |
| Topics: | Site Management, Digital Applications, Quality Management, Human Resources, Risk Management, Roles and Professions, Sustainability, Business Strategy, Engineering Principles, Research Practice |
| Descriptive scope: | 5 PCTEA |
N.B. Descriptive scope is a count of how many of the five facets of empirical research are indicated by the words used in title, abstract and keywords. It is not intended as a judgement on the research; merely a count of the kind of word we would expect to indicate Phenomenon, Concepts, Theoretical framing, Empirical techniques, Analytical techniques. If all five are present, then a code of “5 PCTEA” will indicate this. If you feel the coding for this record is questionable, we welcome discussion around the terms we matched or the way we categorized them. The facet you would expect may not be coded, or a facet may be coded inappropriately. This can also bear on a larger question, of which facets should be treated as defining in construction management research. Please get in touch, and we will look at it. More details here