Application of neural network techniques to fault diagnosis of a heat exchanger system
Artificial neural networks, by their superior pattern-recognition ability, are well-suited for developing intelligent diagnostic tools for complex processes such as process plant operation. Fault diagnosis in a cross-flow tubular heat exchanger system is carried out by using three different paradigm...
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| Other Authors: | |
| Format: | Thesis |
| Language: | English |
| Published: |
2009
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| Online Access: | http://hdl.handle.net/10356/19859 |
| _version_ | 1811683034864287744 |
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| author | Woon, Kok Meng. |
| author2 | Ho, Hiang Kwee |
| author_facet | Ho, Hiang Kwee Woon, Kok Meng. |
| author_sort | Woon, Kok Meng. |
| collection | NTU |
| description | Artificial neural networks, by their superior pattern-recognition ability, are well-suited for developing intelligent diagnostic tools for complex processes such as process plant operation. Fault diagnosis in a cross-flow tubular heat exchanger system is carried out by using three different paradigms - the Backpropagation (BP) network, the Recurrent Cascade-Correlation (RCC) network and the Self-Organising Map (SOM). The study focusses on two different fault scenarios which are simulated for the heat exchanger plant. The first deals with distinct fault states caused by equipment failure within the system whilst the other deals with fouling in the heat exchanger tubes. Training and performance results obtained from a comparative study using the three different networks are presented and practical issues concerning their role and implementation are discussed. |
| first_indexed | 2024-10-01T04:06:19Z |
| format | Thesis |
| id | ntu-10356/19859 |
| institution | Nanyang Technological University |
| language | English |
| last_indexed | 2024-10-01T04:06:19Z |
| publishDate | 2009 |
| record_format | dspace |
| spelling | ntu-10356/198592023-03-11T16:58:41Z Application of neural network techniques to fault diagnosis of a heat exchanger system Woon, Kok Meng. Ho, Hiang Kwee School of Mechanical and Production Engineering DRNTU::Engineering::Electrical and electronic engineering::Computer hardware, software and systems Artificial neural networks, by their superior pattern-recognition ability, are well-suited for developing intelligent diagnostic tools for complex processes such as process plant operation. Fault diagnosis in a cross-flow tubular heat exchanger system is carried out by using three different paradigms - the Backpropagation (BP) network, the Recurrent Cascade-Correlation (RCC) network and the Self-Organising Map (SOM). The study focusses on two different fault scenarios which are simulated for the heat exchanger plant. The first deals with distinct fault states caused by equipment failure within the system whilst the other deals with fouling in the heat exchanger tubes. Training and performance results obtained from a comparative study using the three different networks are presented and practical issues concerning their role and implementation are discussed. Master of Engineering (MPE) 2009-12-14T06:58:51Z 2009-12-14T06:58:51Z 1996 1996 Thesis http://hdl.handle.net/10356/19859 en NANYANG TECHNOLOGICAL UNIVERSITY 123 p. application/pdf |
| spellingShingle | DRNTU::Engineering::Electrical and electronic engineering::Computer hardware, software and systems Woon, Kok Meng. Application of neural network techniques to fault diagnosis of a heat exchanger system |
| title | Application of neural network techniques to fault diagnosis of a heat exchanger system |
| title_full | Application of neural network techniques to fault diagnosis of a heat exchanger system |
| title_fullStr | Application of neural network techniques to fault diagnosis of a heat exchanger system |
| title_full_unstemmed | Application of neural network techniques to fault diagnosis of a heat exchanger system |
| title_short | Application of neural network techniques to fault diagnosis of a heat exchanger system |
| title_sort | application of neural network techniques to fault diagnosis of a heat exchanger system |
| topic | DRNTU::Engineering::Electrical and electronic engineering::Computer hardware, software and systems |
| url | http://hdl.handle.net/10356/19859 |
| work_keys_str_mv | AT woonkokmeng applicationofneuralnetworktechniquestofaultdiagnosisofaheatexchangersystem |