The use of fault detection and diagnostics to reduce air handling unit energy consumption

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dc.check.date10000-01-01
dc.check.embargoformatBoth hard copy thesis and e-thesisen
dc.check.entireThesisEntire Thesis Restricted
dc.check.infoIndefiniteen
dc.check.opt-outNoen
dc.check.reasonReleasing this thesis would cause substantial prejudice to the commercial interests of University College Corken
dc.contributor.advisorO'Sullivan, Dominicen
dc.contributor.authorBruton, Ken
dc.date.accessioned2015-08-28T14:22:44Z
dc.date.issued2014
dc.date.submitted2014
dc.description.abstractThe contribution of buildings towards total worldwide energy consumption in developed countries is between 20% and 40%. Heating Ventilation and Air Conditioning (HVAC), and more specifically Air Handling Units (AHUs) energy consumption accounts on average for 40% of a typical medical device manufacturing or pharmaceutical facility’s energy consumption. Studies have indicated that 20 – 30% energy savings are achievable by recommissioning HVAC systems, and more specifically AHU operations, to rectify faulty operation. Automated Fault Detection and Diagnosis (AFDD) is a process concerned with potentially partially or fully automating the commissioning process through the detection of faults. An expert system is a knowledge-based system, which employs Artificial Intelligence (AI) methods to replicate the knowledge of a human subject matter expert, in a particular field, such as engineering, medicine, finance and marketing, to name a few. This thesis details the research and development work undertaken in the development and testing of a new AFDD expert system for AHUs which can be installed in minimal set up time on a large cross section of AHU types in a building management system vendor neutral manner. Both simulated and extensive field testing was undertaken against a widely available and industry known expert set of rules known as the Air Handling Unit Performance Assessment Rules (APAR) (and a later more developed version known as APAR_extended) in order to prove its effectiveness. Specifically, in tests against a dataset of 52 simulated faults, this new AFDD expert system identified all 52 derived issues whereas the APAR ruleset identified just 10. In tests using actual field data from 5 operating AHUs in 4 manufacturing facilities, the newly developed AFDD expert system for AHUs was shown to identify four individual fault case categories that the APAR method did not, as well as showing improvements made in the area of fault diagnosis.en
dc.description.statusNot peer revieweden
dc.description.versionAccepted Version
dc.format.mimetypeapplication/pdfen
dc.identifier.citationBruton, K. 2014. The use of fault detection and diagnostics to reduce air handling unit energy consumption. PhD Thesis, University College Cork.en
dc.identifier.endpage226
dc.identifier.urihttps://hdl.handle.net/10468/1949
dc.language.isoenen
dc.publisherUniversity College Corken
dc.rights© 2014, Ken Bruton.en
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/3.0/en
dc.subjectHVACen
dc.subjectEnergy efficiencyen
dc.subjectFault detection and diagnosticsen
dc.subjectExpert systemsen
dc.subjectBuilding management systemsen
dc.subjectAir handling unitsen
dc.thesis.opt-outfalse
dc.titleThe use of fault detection and diagnostics to reduce air handling unit energy consumptionen
dc.typeDoctoral thesisen
dc.type.qualificationlevelDoctoralen
dc.type.qualificationnamePHD (Engineering)en
ucc.workflow.supervisordominic.osullivan@ucc.ie
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