Automated Fault Detection and Diagnosis of AHUs via Tabular-Based Methods Using Operational Data from a Large Office Building

Implementing automated fault detection and diagnosis (AFDD) for air handling units (AHUs) is crucial for maintaining optimal indoor air quality and extending the operational life of equipment. However, previous studies often encountered challenges arising from limited real-world operational data and difficulties in accurately labeling fault conditions. Additionally, tabular-based methods, despite exhibiting robust performance in various applications, have been relatively underexplored in AFDD research. To address these research gaps, this study focuses on constant air volume (CAV) AHUs that operated continuously in a large-scale office building for 1 year. Data were collected from 18 sensors installed across…