Development of a Unified Taxonomy for HVAC System Faults

Detecting and diagnosing HVAC faults is critical for maintaining building operation performance, reducing energy waste, ensuring indoor comfort. An increasing deployment of commercial fault detection diagnostics (FDD) software tools in buildings the past decade has significantly increased buildings’ operational reliability reduced consumption. A massive amount data been generated by FDD tools. However, efficiently utilizing ‘big data’ analytics, algorithm improvement, other data-driven applications challenging because format naming conventions those are very customized, unstructured, hard to interpret. This paper presents development a unified taxonomy faults. an orderly classification according their characteristics causal relations. The includes categorization, physical hierarchy, library, relation model, naming/tagging scheme. employs both hierarchy equipment cause-effect relationship model reveal root causes systems. structured standardized vocabulary library developed increase representability interpretability. can be used system analytics such as prevalence analysis or standard. common type equipment-packaged rooftop unit (RTU) example demonstrate application taxonomy. Two RTU show that after mapping taxonomy, meta-analysis multiple reports possible efficient.