Fault detection and diagnosis for chiller system
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...
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| Format: | Final Year Project (FYP) |
| Language: | English |
| Published: |
2017
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| Online Access: | http://hdl.handle.net/10356/70744 |
| _version_ | 1811685080805933056 |
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| author | Aribowo, Andrew Ixara |
| author2 | Cai Wenjian |
| author_facet | Cai Wenjian Aribowo, Andrew Ixara |
| author_sort | Aribowo, Andrew Ixara |
| collection | NTU |
| description | 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. |
| first_indexed | 2024-10-01T04:38:50Z |
| format | Final Year Project (FYP) |
| id | ntu-10356/70744 |
| institution | Nanyang Technological University |
| language | English |
| last_indexed | 2024-10-01T04:38:50Z |
| publishDate | 2017 |
| record_format | dspace |
| spelling | ntu-10356/707442023-07-07T17:03:14Z Fault detection and diagnosis for chiller system Aribowo, Andrew Ixara Cai Wenjian Wang Youyi School of Electrical and Electronic Engineering DRNTU::Engineering::Electrical and electronic engineering 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. Bachelor of Engineering 2017-05-09T08:52:31Z 2017-05-09T08:52:31Z 2017 Final Year Project (FYP) http://hdl.handle.net/10356/70744 en Nanyang Technological University 69 p. application/pdf |
| spellingShingle | DRNTU::Engineering::Electrical and electronic engineering Aribowo, Andrew Ixara Fault detection and diagnosis for chiller system |
| title | Fault detection and diagnosis for chiller system |
| title_full | Fault detection and diagnosis for chiller system |
| title_fullStr | Fault detection and diagnosis for chiller system |
| title_full_unstemmed | Fault detection and diagnosis for chiller system |
| title_short | Fault detection and diagnosis for chiller system |
| title_sort | fault detection and diagnosis for chiller system |
| topic | DRNTU::Engineering::Electrical and electronic engineering |
| url | http://hdl.handle.net/10356/70744 |
| work_keys_str_mv | AT aribowoandrewixara faultdetectionanddiagnosisforchillersystem |