Abstract

Automated Fault Detection and Diagnosis (AFDD) of building mechanical systems, including HVAC
(Heating, Ventilation, and Air Conditioning), has received substantial attention recently from both research
and application angles. The reasons are attributed to potential savings in energy consumption and
maintenance. Various methods, including simulation and Grey-Box, are offered, but data-driven ones have
received the most attention due to reduced manual effort, integrability, and scalability. Accordingly, to
enhance energy efficiency and reduce operational costs, various Machine Learning (ML) models have
been developed for AFDD of HVAC systems. However, the implementation of such data-driven
approaches has often translated into a loss of contextual data. This study integrates operational data with
building information and its various disciplines, linking the two to facilitate AFDD model development. BIM
(Building Information Model) and BAS/BMS (Building Automation System/Building Management System)
data are the repositories utilized for this integration.
The proposed solution integrates bottom-up (data-driven via Machine Learning) and top-down
(knowledge-oriented via Semantic Web Technologies) AI approaches to generate an effective AFDD
knowledge model. The study materializes a two-way flow of data and knowledge between the BIM and
BMS by utilizing an ontology named AFDDOnto, which integrates building components with fault types,
methods, and parameters. The solution enables AFDD algorithms to utilize static and dynamic information
related to HVAC and building spaces to develop enriched AFDD models. It incorporates building spatial
information and stores analytics to represent the facility's as-is state. The proposed BIM-based knowledge
solution can be used for AFDD model development, tracking changes, and analysis and visualization in
two ways. Firstly, to integrate the BIM features with BMS features for creating ‘context-aware’ AFDD
models. Secondly, to semantically store BIM-based AFDD performance analytics through AFDDOnto that
can be used for model comparison, reproduction and visualization through knowledge graphs.
The knowledge stored in the repository can be queried, which enables access to contextual information
(knowledge graphs, images, videos, project snippets); spatial data (locations, states); and apriori
knowledge (configuration and analytics) to enable development, application, and visualization of context-
aware AFDD models. Additionally, the proposed solution can maintain access to external project files and
databases to enable interoperability between BIM and BAS/BMS. The potential users include HVAC
operators, BIM Managers, and Facility Managers tasked with the operation and maintenance of HVAC
systems.