Peer-Reviewed Journal Details
Mandatory Fields
Bruton, K., Raftery, P., O'Donovan Peter., Aughney, Niall., Keane, Marcus M., & O'Sullivan, D.T.J.
2014
Unknown
Automation In Construction
Development and alpha testing of a cloud based automated fault detection and diagnosis tool for Air Handling Units.
Published
()
Optional Fields
AHU Fault Detection and Diagnosis (FDD),APAR,HVAC system energy efficiency,BMS data acquisition,On-going commissioning
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Heating Ventilation and Air Conditioning (HVAC) system energy consumption on average accounts for 40% of an industrial sites total energy consumption. Studies have indicated that 20 30% energy savings are achievable by recommissioning Air Handling Units (AHUs) in HVAC systems to rectify faulty operation. Studies have also demonstrated that on-going commissioning of building systems for optimum efficiency can yield savings of an average of over 20% of total energy cost. Automated Fault Detection and Diagnosis (AFDD) is a process concerned with automating the detection of faults and their causes in physical systems. AFDD can be used to assist the commissioning process at multiple stages. This paper outlines the development of an AFDD tool for AHUs using expert rules. It outlines the results of the alpha testing phase of the tool on 18 AHUs across four commercial & industrial sites with over 104,000 annual energy savings detected by the AFDD tool.
10.1016/j.autcon.2013.12.006.
Grant Details
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