Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/70744
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dc.contributor.authorAribowo, Andrew Ixara
dc.date.accessioned2017-05-09T08:52:31Z
dc.date.available2017-05-09T08:52:31Z
dc.date.issued2017
dc.identifier.urihttp://hdl.handle.net/10356/70744
dc.description.abstractHVAC system is an important part of every structure where it also uses a lot of power consumption. In all HVAC system component, the chiller is one of the most power consuming component, thus, any fault that occurs in a chiller might increase the chiller power consumption and lead to inefficiency of the overall HVAC system. The solution to this problem is fault detection and diagnosis (FDD) which makes it possible to detect the fault and diagnose which kind of fault occurs. This approach reduces the time in finding the fault, thus, reduces the energy consumption during fault and preventing mechanical damages to the chiller. This project focuses on using neural networks as the FDD approach for the chiller system, where the correct detection percentage of faults are calculated using a system with 2 neural networks for regression and classification respectively.en_US
dc.format.extent69 p.en_US
dc.language.isoenen_US
dc.rightsNanyang Technological University
dc.subjectDRNTU::Engineering::Electrical and electronic engineeringen_US
dc.titleFault detection and diagnosis for chiller systemen_US
dc.typeFinal Year Project (FYP)en_US
dc.contributor.supervisorCai Wenjianen_US
dc.contributor.supervisorWang Youyien_US
dc.contributor.schoolSchool of Electrical and Electronic Engineeringen_US
dc.description.degreeBachelor of Engineeringen_US
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Appears in Collections:EEE Student Reports (FYP/IA/PA/PI)
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