Development of FDD-ON: an Ontology for VAV HVAC System Fault Detection and Diagnostics

2026-07-31

AI Summary

This study addresses the challenge of information silos in fault detection and diagnosis (FDD) for variable air volume (VAV) HVAC systems, which arise from data heterogeneity and semantic ambiguity. To this end, the authors propose FDD-ON, the first structured semantic framework specifically designed for this domain. Built upon modular ontology engineering and knowledge graph techniques, FDD-ON formally models system components, fault types, symptoms, impacts, and their causal relationships, providing a unified controlled vocabulary and a comprehensive knowledge base. The framework enables machine-interpretable diagnostic reasoning and enhances system interoperability. Experimental validation on public datasets demonstrates that FDD-ON serves as a foundational semantic infrastructure for developing transparent, scalable, and AI-driven FDD applications.

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.

Problem

• fault detection and diagnostics
• HVAC systems
• interoperability
• data interpretability
• ontology

Innovation

• Ontology
• Fault Detection and Diagnostics (FDD)
• HVAC Systems
• Interoperability
• Semantic Framework