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

Abstract

AbstractImplementing 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 ...Practical ApplicationsThis research demonstrates the effectiveness of advanced machine learning methods—TabNet and TabTransformer—in detecting faults in air handling units (AHUs), critical components within HVAC systems commonly found in office buildings. ...