Development of FDD-ON: an Ontology for VAV HVAC System Fault Detection and Diagnostics
2026-07-31 • Artificial Intelligence
Artificial Intelligence
AI summaryⓘ
The authors created a structured system called FDD-ON to help describe and connect information about faults in variable air volume (VAV) HVAC systems. This system organizes knowledge about parts, faults, symptoms, and their effects in a way that computers can understand and share. By linking causes, faults, symptoms, and impacts clearly, their work helps make fault detection and diagnosis more consistent and usable across different tools. They tested FDD-ON with real data and showed it can improve the development of fault detection applications. Overall, their ontology helps make HVAC fault diagnosis smarter and easier to integrate.
Fault Detection and Diagnosis (FDD)HVACVariable Air Volume (VAV)OntologySemantic frameworkInteroperabilityDigital twinArtificial intelligence (AI)Controlled vocabularyMachine-interpretable
Authors
Yimin Chen, Brian Fricke, Bo Shen, Jamie Lian, Mingkan Zhang, James Lo, Yun Zhang, Shi Ye, Jiajing Huang, Han Hu, Chujie Lu, Rui Tang, George Zhuang
Abstract
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.