Development of FDD-ON: an Ontology for VAV HVAC System Fault Detection and Diagnostics
Organizations: Building Technologies Research and Integration Center, Oak Ridge National Laboratory, Oak Ridge, TN 37830, U.S. · Civil, Architectural and Environmental Engineering Department, Drexel University, 3141 Chestnut St. Philadelphia, PA 94706, U.S. · Lu+S Engineers. 4924 Dominion Blvd, Glen Allen, VA 23060, U.S. · Merck & Co. 126 E Lincoln Ave, Rahway, NJ 07065, U.S. · School of Data Science and Analytics, Kennesaw State University, Marietta, GA 30060, U.S. · Department of Mechanical Engineering, University of Arkansas, Fayetteville, AR 72701, U.S. · Department of Architectural Engineering and Technology, Delft University of Technology, Julianalaan 134, 2628 BL Delft, Netherlands · Institute for Environmental Design and Engineering, The Bartlett, University College London, London WC1H 0NN, U.K. · Faculty of Mathematics, University of Waterloo, Waterloo, ON N2L 3G1, Canada
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.