Sensor placement for fault diagnosis using genetic algorithm
This paper presents a novel methodology for the purpose of fault detection and isolation (FDI) to a two-tank system. This new methodology benefits from the basic facts that faults are embedded in the analytical redundancy relations (ARRs) and that the occurrence of a fault will cause the correspondi...
| Main Authors: | , , , , , |
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| Other Authors: | |
| Format: | Conference Paper |
| Language: | English |
| Published: |
2013
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| Subjects: | |
| Online Access: | https://hdl.handle.net/10356/102788 http://hdl.handle.net/10220/16441 |
| _version_ | 1826114223313256448 |
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| author | Chi, Guoyi Wang, Danwei Yu, Ming Alavi, Marjan Le, Tung Luo, Ming |
| author2 | School of Electrical and Electronic Engineering |
| author_facet | School of Electrical and Electronic Engineering Chi, Guoyi Wang, Danwei Yu, Ming Alavi, Marjan Le, Tung Luo, Ming |
| author_sort | Chi, Guoyi |
| collection | NTU |
| description | This paper presents a novel methodology for the purpose of fault detection and isolation (FDI) to a two-tank system. This new methodology benefits from the basic facts that faults are embedded in the analytical redundancy relations (ARRs) and that the occurrence of a fault will cause the corresponding ARRs to change. Based on these facts, the minimal isolation set as an important concept is introduced to make each fault in the fault set F detectable and isolable. Then, the sensor placement problem consists in determining an optimal minimal isolation set associated with the least number of sensors. A dedicated genetic algorithm is developed to solve the formulated sensor placement problem. A case study of a two-tank system shows that the proposed methodology performs well. |
| first_indexed | 2024-10-01T03:35:58Z |
| format | Conference Paper |
| id | ntu-10356/102788 |
| institution | Nanyang Technological University |
| language | English |
| last_indexed | 2024-10-01T03:35:58Z |
| publishDate | 2013 |
| record_format | dspace |
| spelling | ntu-10356/1027882020-03-07T13:24:51Z Sensor placement for fault diagnosis using genetic algorithm Chi, Guoyi Wang, Danwei Yu, Ming Alavi, Marjan Le, Tung Luo, Ming School of Electrical and Electronic Engineering IEEE Conference on Emerging Technologies & Factory Automation (17th : 2012 : Krakow, Poland) A*STAR SIMTech DRNTU::Engineering::Electrical and electronic engineering This paper presents a novel methodology for the purpose of fault detection and isolation (FDI) to a two-tank system. This new methodology benefits from the basic facts that faults are embedded in the analytical redundancy relations (ARRs) and that the occurrence of a fault will cause the corresponding ARRs to change. Based on these facts, the minimal isolation set as an important concept is introduced to make each fault in the fault set F detectable and isolable. Then, the sensor placement problem consists in determining an optimal minimal isolation set associated with the least number of sensors. A dedicated genetic algorithm is developed to solve the formulated sensor placement problem. A case study of a two-tank system shows that the proposed methodology performs well. 2013-10-10T08:43:15Z 2019-12-06T21:00:13Z 2013-10-10T08:43:15Z 2019-12-06T21:00:13Z 2012 2012 Conference Paper Chi, G., Wang, D., Yu, M., Alavi, M., Le, T., & Luo, M. (2012). Sensor placement for fault diagnosis using genetic algorithm. 2012 IEEE 17th Conference on Emerging Technologies & Factory Automation (ETFA), pp.1-7. https://hdl.handle.net/10356/102788 http://hdl.handle.net/10220/16441 10.1109/ETFA.2012.6489615 en © 2012 IEEE |
| spellingShingle | DRNTU::Engineering::Electrical and electronic engineering Chi, Guoyi Wang, Danwei Yu, Ming Alavi, Marjan Le, Tung Luo, Ming Sensor placement for fault diagnosis using genetic algorithm |
| title | Sensor placement for fault diagnosis using genetic algorithm |
| title_full | Sensor placement for fault diagnosis using genetic algorithm |
| title_fullStr | Sensor placement for fault diagnosis using genetic algorithm |
| title_full_unstemmed | Sensor placement for fault diagnosis using genetic algorithm |
| title_short | Sensor placement for fault diagnosis using genetic algorithm |
| title_sort | sensor placement for fault diagnosis using genetic algorithm |
| topic | DRNTU::Engineering::Electrical and electronic engineering |
| url | https://hdl.handle.net/10356/102788 http://hdl.handle.net/10220/16441 |
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