Eine Plattform für die Wissenschaft: Bauingenieurwesen, Architektur und Urbanistik
Eine Plattform für die Wissenschaft: Bauingenieurwesen, Architektur und Urbanistik
An ontology for automated fault detection & diagnostics of HVAC using BIM and machine learning concepts
This paper presents an ontology for AFDD (Automated Fault Detection and Diagnostics) of HVAC (Heating, Ventilation, and Air conditioning) systems in buildings called “AFDDOnto”. Presently, the AFDD models are mainly data-centric and often lack semantic information such as contextual information and spatial information; additionally, configuration information, analysis, and results used for model development are lost once developed. This impedes an effective mechanism for tracking changes and updating the model for future developments and use cases. The Proposed ontology can be used for AFDD model development, tracking changes, analytics, visualization, and digital twinning by enabling integration of BIM with BAS/BMS (Building Automation System/Building Management System) concepts and secondly to store AFDD configuration and analytics in the AFDDOnto. Select competency questions are constructed using SPARQL queries to access the proposed knowledge model. The proposed ontology has been tested against different measures using multiple metrics and a case study and further validated using a semi-structured survey of experts. Applied AI engineers, Facility managers, Asset managers, and building owners aiming to develop AFDD models for HVAC systems can benefit from adopting this ontology for HVAC maintenance, including analysis, model development, and knowledge management.
An ontology for automated fault detection & diagnostics of HVAC using BIM and machine learning concepts
This paper presents an ontology for AFDD (Automated Fault Detection and Diagnostics) of HVAC (Heating, Ventilation, and Air conditioning) systems in buildings called “AFDDOnto”. Presently, the AFDD models are mainly data-centric and often lack semantic information such as contextual information and spatial information; additionally, configuration information, analysis, and results used for model development are lost once developed. This impedes an effective mechanism for tracking changes and updating the model for future developments and use cases. The Proposed ontology can be used for AFDD model development, tracking changes, analytics, visualization, and digital twinning by enabling integration of BIM with BAS/BMS (Building Automation System/Building Management System) concepts and secondly to store AFDD configuration and analytics in the AFDDOnto. Select competency questions are constructed using SPARQL queries to access the proposed knowledge model. The proposed ontology has been tested against different measures using multiple metrics and a case study and further validated using a semi-structured survey of experts. Applied AI engineers, Facility managers, Asset managers, and building owners aiming to develop AFDD models for HVAC systems can benefit from adopting this ontology for HVAC maintenance, including analysis, model development, and knowledge management.
An ontology for automated fault detection & diagnostics of HVAC using BIM and machine learning concepts
Hosseini Gourabpasi, Arash (Autor:in) / Nik-Bakht, Mazdak (Autor:in)
Science and Technology for the Built Environment ; 30 ; 972-988
13.09.2024
17 pages
Aufsatz (Zeitschrift)
Elektronische Ressource
Englisch
Automated Fault Detection and Diagnostics for the HVAC&R Industry
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