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.

Commercial building HVAC systems experience many sensing, mechanical, and control-related faults that increase energy consumption and impact occupant comfort. Fault detection & diagnostics (FDD) software has been demonstrated to identify and help diagnose these types of faults. Several studies have demonstrated FDD energy savings potential, but there is limited empirical data characterizing the quantity and type of faults reported by FDD tools. This paper presents results of an FDD fault reporting study, employing multi-year monitoring data for over 60,000 pieces of HVAC equipment, covering over 90 fault types, and using new metrics that we developed to characterize fault prevalence. Study results offer an unprecedented accounting of the quantity of faults reported, the most commonly occurring faults, and fault persistence. We find that 21 air handling unit (AHU) faults were reported on 20% or more AHUs in our dataset, and 18 AHU faults persisted for more than 20% of the time period covered by the data. On any given day, 40% of AHUs and 30% of air terminal units saw a reported fault of some kind. Based on in-depth analysis of these results we provide recommendations for building operators, FDD software developers, and researchers to enable more efficient commercial building operation.