PhD Oral Exam - Arash Hosseini Gourabpasi, Civil Engineering

When studying for a doctoral degree (PhD), candidates submit a thesis that provides a critical review of the current state of knowledge of the thesis subject as well as the student’s own contributions to the subject. The distinguishing criterion of doctoral graduate research is a significant and original contribution to knowledge.

Once accepted, the candidate presents the thesis orally. This oral exam is open to the public.

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. The proposed method saves its users time for AFDD model development and creates a point of reference for model comparison. The proposed solution can be deployed in buildings with limited sensors to enable AFDD model development. Additionally, BIM can be utilized to generate context-aware features that can be used to identify HVAC faults. The suggested future direction of this research is the coverage of additional competencies and further investigation of the applicability of the proposed method to other BIM uses.