Development of a Unified Taxonomy for HVAC System Faults
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 significantl...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2021-09-01
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| Series: | Energies |
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| Online Access: | https://www.mdpi.com/1996-1073/14/17/5581 |
| _version_ | 1797521391773286400 |
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| author | Yimin Chen Guanjing Lin Eliot Crowe Jessica Granderson |
| author_facet | Yimin Chen Guanjing Lin Eliot Crowe Jessica Granderson |
| author_sort | Yimin Chen |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-10T08:11:56Z |
| format | Article |
| id | doaj.art-af2e36c31c0e4401963242ff2503a91b |
| institution | Directory Open Access Journal |
| issn | 1996-1073 |
| language | English |
| last_indexed | 2024-03-10T08:11:56Z |
| publishDate | 2021-09-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Energies |
| spelling | doaj.art-af2e36c31c0e4401963242ff2503a91b2023-11-22T10:36:54ZengMDPI AGEnergies1996-10732021-09-011417558110.3390/en14175581Development of a Unified Taxonomy for HVAC System FaultsYimin Chen0Guanjing Lin1Eliot Crowe2Jessica Granderson3Building Technology & Urban Systems Division, Lawrence Berkeley National Laboratory, Berkeley, CA 94720, USABuilding Technology & Urban Systems Division, Lawrence Berkeley National Laboratory, Berkeley, CA 94720, USABuilding Technology & Urban Systems Division, Lawrence Berkeley National Laboratory, Berkeley, CA 94720, USABuilding Technology & Urban Systems Division, Lawrence Berkeley National Laboratory, Berkeley, CA 94720, USADetecting 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.https://www.mdpi.com/1996-1073/14/17/5581fault taxonomyfault detection and diagnosticsbig data analyticssemantic analysisbuilding informatics |
| spellingShingle | Yimin Chen Guanjing Lin Eliot Crowe Jessica Granderson Development of a Unified Taxonomy for HVAC System Faults Energies fault taxonomy fault detection and diagnostics big data analytics semantic analysis building informatics |
| title | Development of a Unified Taxonomy for HVAC System Faults |
| title_full | Development of a Unified Taxonomy for HVAC System Faults |
| title_fullStr | Development of a Unified Taxonomy for HVAC System Faults |
| title_full_unstemmed | Development of a Unified Taxonomy for HVAC System Faults |
| title_short | Development of a Unified Taxonomy for HVAC System Faults |
| title_sort | development of a unified taxonomy for hvac system faults |
| topic | fault taxonomy fault detection and diagnostics big data analytics semantic analysis building informatics |
| url | https://www.mdpi.com/1996-1073/14/17/5581 |
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