Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/70744
Title: Fault detection and diagnosis for chiller system
Authors: Aribowo, Andrew Ixara
Keywords: DRNTU::Engineering::Electrical and electronic engineering
Issue Date: 2017
Abstract: HVAC 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.
URI: http://hdl.handle.net/10356/70744
Rights: Nanyang Technological University
Fulltext Permission: restricted
Fulltext Availability: With Fulltext
Appears in Collections:EEE Student Reports (FYP/IA/PA/PI)

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