A new integrated framework to fault detection and diagnosis of air handling unit: Emphasizing the impact of symptoms

This study proposes a comprehensive framework for Fault Detection and Diagnosis (FDD) in Air Handling Units (AHU), emphasizing the impact of symptoms associated with various faults. The aim is to address an important limitation in FDD model development where detection is based simply on fault intensity without considering symptom impact or subjective severity criteria across faults. Instead, a new approach is introduced that factors in fault impact, utilizing impact analysis to enhance FDD accuracy and efficiency. The methodology involves three interconnected components: First, a fault impact analysis categorizes 18 fault types into stages per intensity and assesses resulting symptom impacts. Second, symptom-based intensity thresholds are established to categorize fault severity into three levels based on symptom severity. Finally, the goal is to integrate these findings into the FDD process for optimized fault detection and diagnosis. The study successfully categorized 18 faults into three severity levels, with thresholds identified for each. Furthermore, Tree-based models demonstrated effective performance when conducting FDD based on these levels. The approach provides a clear framework through three linked processes. This research combines comprehensive impact analysis with sophisticated classification methods, presenting a more defined, systematic, and efficient FDD approach. This novel methodology substantially advances fault detection and diagnosis, especially for building energy systems.