构建可扩展的暖通空调故障诊断本体，提升系统互操作性
Development of FDD-ON: an Ontology for VAV HVAC System Fault Detection and Diagnostics
- 提出模块化本体FDD-ON，统一描述变风量系统组件与故障关系
- 涵盖故障、症状、影响全链条属性，支持机器可读查询与数据映射
- 适用于数字孪生和智能运维系统，助力跨平台故障诊断落地
故障检测与诊断（FDD）技术对提升暖通空调系统可靠性、能效及维护效果至关重要。然而，现有FDD方案受限于领域知识结构不统一，难以整合异构数据源、多样化设备类型及不同诊断输出，导致信息孤岛，阻碍数字孪生驱动的FDD框架与人工智能辅助决策系统的应用。本文提出一种可扩展的暖通空调系统故障检测与诊断本体（FDD-ON），用于形式化表示变风量（VAV）系统组件、故障类型、症状状态、故障影响及相关属性。FDD-ON通过规范词汇表整合系统语义，提供全面的故障、症状与影响知识库，明确表达“根本原因—故障—症状—影响”链路关系，为诊断知识查询、异构输出映射及可互操作的FDD应用开发提供机器可读基础。基于公开的VAV HVAC系统数据集进行评估，并在多个FDD开发场景中验证。结果表明，FDD-ON为各类应用提供了可扩展、透明且互操作的语义框架。
原文摘要 · Abstract (English)
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.
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