Analysis of Automated Fault Detection and Diagnostics Records as an Indicator of HVAC Fault Prevalence: Methodology and Preliminary Results
Commercial buildings, Fault prevalence, FDD, Fault taxonomy
Faults in commercial buildings can cause energy waste and other performance problems such as reduced occupant comfort, reduced equipment longevity, and increased noise. However, it is currently unknown how commonly faults occur in different equipment types. This paper describes a method to estimate the prevalence of faults in air handling units, air terminal units, and rooftop units and the use of three metrics for summarizing results. This method was developed by the authors as part of a study which includes data from several automated fault detection and diagnostics (AFDD) data providers, providing a large sample with a wide range of building types, geographical locations, and equipment types. This dataset includes fault diagnoses from thousands of buildings throughout the United States, as well as anonymized metadata describing the building and equipment characteristics. The number of fault records is on the order of 106. We describe here how the data from different data providers can be processed and unified using a common taxonomy, and illustrate three metrics that can provide insights using this type of data. The methods developed for this study are illustrated here with preliminary data. This work supports a multi-year, multi-institutional project that will provide insight into the drivers of fault prevalence; for example, whether prevalence is correlated with characteristics like building type, building size, and geographical location (including related factors like local climate and utility rates). We discuss some of the challenges of harmonizing disparate outputs from multiple AFDD providers, the usefulness of applying a unifying fault taxonomy, and provide preliminary figures that illustrate three fault prevalence metrics.