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.

中文翻译：

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支持数字孪生的异常检测，用于运营和维护中的内置资产监控

摘要 有效的资产管理在提供建筑物的功能性和适用性方面发挥着重要作用。然而，缺乏有效的策略和综合方法来管理资产及其相关数据，以帮助监控、检测、记录和沟通运营和维护 (O&M) 问题。随着数字孪生 (DT) 概念在建筑、工程、施工和设施管理 (AEC/FM) 领域的重要性得到证实，支持 DT 的资产监控异常检测系统及其基于扩展行业基础类的数据集成方法(IFC) 在日常运维管理中提供了本研究。本文提出了一种新颖的基于 IFC 的数据结构，使用它从建筑物 DT 中提取一组带有资产运行状况诊断信息的监控数据。考虑到资产在由人类需求决定的不断变化的负载下运行，采用处理运营数据上下文特征的贝叶斯变化点检测方法，通过与外部运营信息的交叉引用来识别和过滤上下文异常。以暖通空调（HVAC）系统中的离心泵为例，结果表明并证明了基于 DT 的新型异常检测流程实现了对泵的连续异常检测，有助于高效和运维自动化资产监控。考虑到资产在由人类需求决定的不断变化的负载下运行，采用处理运营数据上下文特征的贝叶斯变化点检测方法，通过与外部运营信息的交叉引用来识别和过滤上下文异常。以暖通空调（HVAC）系统中的离心泵为例，结果表明并证明了基于 DT 的新型异常检测流程实现了对泵的连续异常检测，有助于高效和运维自动化资产监控。考虑到资产在由人类需求决定的不断变化的负载下运行，采用处理运营数据上下文特征的贝叶斯变化点检测方法，通过与外部运营信息的交叉引用来识别和过滤上下文异常。以暖通空调（HVAC）系统中的离心泵为例，结果表明并证明了基于 DT 的新型异常检测流程实现了对泵的连续异常检测，有助于高效和运维自动化资产监控。