BIM-based automated fault detection and diagnostics of HVAC systems in commercial buildings

Abstract

In order to meet the growing demand for effective Automated Fault Detection and Diagnostics (AFDD) for HVAC systems, innovative approaches are needed to address limitations in data diversity and access to contextual information. This study introduces a methodology that leverages Building Information Modeling (BIM) to enhance the development of the AFDD model. Feature engineering techniques are utilized to generate dynamic BIM features, compensating for the lack of sensory and contextual data in Building Management Systems (BMS). By integrating AFDD analytics with BIM, a comprehensive digital twin of the facility is created, which enables facility managers to compare, reuse, and develop AFDD models for HVAC systems. The proposed methodology demonstrates the potential of leveraging BIM-based knowledge models to overcome the challenges associated with the limited sensor and contextual information availability by utilizing BIM for feature generation and, conversely, updating the BIM model with AFDD analytics.