AI 中文总结
该研究开发了用于VAV HVAC系统故障检测与诊断的本体FDD-ON，整合相关语义与库，经公开数据集评估，为推进可扩展的FDD解决方案提供了基础语义框架。
AI 中文摘要
故障检测与诊断（FDD）技术对于提升暖通空调（HVAC）系统的可靠性、能源效率和维护有效性至关重要。然而，在建筑中有效部署FDD解决方案需要结构化的领域知识，以弥合异构数据源、不同设备类型和多样化诊断输出之间的鸿沟。FDD领域内有限的数据可解释性和互操作性导致了信息孤岛的碎片化，阻碍了FDD及相关应用的实施，例如支持数字孪生的FDD框架和人工智能（AI）驱动的维护决策系统。本文提出了FDD本体（FDD-ON），这是一种模块化且可扩展的本体，用于正式表示变风量（VAV）暖通空调（HVAC）系统的组件、故障类型、症状状态、故障影响及相关属性。FDD-ON整合了暖通空调系统FDD语义，以提供故障和症状属性的全面表示，并由定义明确的受控词汇表提供支持。此外，FDD-ON提供全面的故障、症状和影响库，以捕获变风量（VAV）暖通空调系统中广泛的运行异常及其后果。通过明确的成因-故障-症状-影响关系，FDD-ON可作为机器可解释的基础，用于查询诊断知识、映射异构FDD输出以及开发可互操作的FDD相关应用。FDD-ON通过公开可用的变风量（VAV）暖通空调系统数据集进行评估，并通过FDD开发应用进行演示。结果表明，FDD-ON为在各种应用中推进可扩展、透明且可互操作的FDD解决方案提供了基础语义框架。
英文摘要
Fault detection and diagnosis (FDD) technology is essential for improving HVAC system reliability, energy efficiency, and maintenance effectiveness. However, effective deployment of FDD solutions in buildings requires structured domain knowledge that can bridge heterogeneous data sources, diverse equipment types, and varied diagnostic outputs. Limited data interpretability and interoperability within the FDD domain have led to fragmented information silos, hindering the implementation of FDD and related applications, such as the digital twin-enabled FDD frameworks and artificial intelligence (AI)-driven maintenance decision-making systems. This paper presents an FDD Ontology (FDD-ON), a modular and extensible ontology to formally represent variable air volume (VAV) HVAC system components, fault types, symptom statuses, fault impacts and associated attributes. FDD-ON integrates HVAC system FDD semantics to provide comprehensive representations of fault and symptom attributes, supported by the well-defined controlled vocabulary. Additionally, FDD-ON offers comprehensive fault, symptom, and impact libraries to capture a broad spectrum of operational abnormalities and their consequences in VAV HVAC systems. Through explicit contributing cause-fault-symptom-impact relations, FDD-ON serves as a machine-interpretable basis for querying diagnostic knowledge, mapping heterogeneous FDD outputs, and developing interoperable FDD-related applications. FDD-ON is evaluated using publicly available VAV HVAC system datasets and demonstrated through FDD development applications. Results indicate that FDD-ON provides a foundational semantic framework for advancing scalable, transparent, and interoperable FDD solutions across various applications.
Comments39 pages, nine figures and 19 tables
