Abstract
Detecting and diagnosing HVAC faults is critical for maintaining building operation performance, reducing energy waste, and ensuring indoor comfort. An increasing deployment of commercial fault detection and diagnostics (FDD) software tools in commercial buildings in the past decade has significantly increased buildings’ operational reliability and reduced energy consumption. A massive amount of data has been generated by the FDD software tools. However, efficiently utilizing FDD data for ‘big data’ analytics, algorithm improvement, and other data-driven applications is challenging because the format and naming conventions of those data are very customized, unstructured, and hard to interpret. This paper presents the development of a unified taxonomy for HVAC faults. A taxonomy is an orderly classification of HVAC faults according to their characteristics and causal relations. The taxonomy includes fault categorization, physical hierarchy, fault library, relation model, and naming/tagging scheme. The taxonomy employs both a physical hierarchy of HVAC equipment and a cause-effect relationship model to reveal the root causes of faults in HVAC systems. A structured and standardized vocabulary library is developed to increase data representability and interpretability. The developed fault taxonomy can be used for HVAC system ‘big data’ analytics such as HVAC system fault prevalence analysis or the development of an HVAC FDD software standard. A common type of HVAC equipment-packaged rooftop unit (RTU) is used as an example to demonstrate the application of the developed fault taxonomy. Two RTU FDD software tools are used to show that after mapping FDD data according to the taxonomy, the meta-analysis of the multiple FDD reports is possible and efficient.
Highlights
We found that the establishment of a clear and accurate data/fault taxonomy increases the systematic description of data/faults from multiple data sources, and facilitates many applications such as locating faults in a complex system, developing a fault analysis system, improving data interoperability, and interpretability
A large amount of data which reflects faults for equipment and components in HVAC systems has been generated via various fault detection and diagnostics (FDD) tool reports, a lack of a unified fault taxonomy makes it difficult to interpret the FDD report data across various FDD tools
We describe the development of a unified taxonomy for HVAC system faults relating to air handling unit (AHU), air terminal units (ATUs), and rooftop unit (RTU)
Summary
Commercial buildings account for more than 40% of energy consumption in the United States, constituting approximately 5296 billion kWh (18.07 quadrillion Btu) of electricity consumption in 2020 [1]. Faults, malfunctioning control and operation, and poor maintenance account for 15–30% of energy waste in commercial buildings in the United States [2]. Apart from energy waste, decreased building thermal comfort, increased system operation and maintenance costs are observed due to various faults in heating, ventilation and air-conditioning (HVAC) systems [3,4,5]. HVAC system operation, as well as decrease energy consumption and other negative impacts on buildings and occupants [6,7,8]. FDD is designed to detect HVAC system abnormalities, locate the fault causes, and facilitate the analytics on the possible impacts of faults during the operation of a system or equipment. More than 30 commercialized FDD software tools are available in the U.S and the deployment scope has witnessed a rapid increase [8]. A fault can cause a malfunction in one component or system, and lead to a failure of the component or system
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