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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Main Author:	Woon, Kok Meng.
Other Authors:	Ho, Hiang Kwee
Format:	Theses and Dissertations
Language:	English
Published:	2009
Subjects:	DRNTU::Engineering::Electrical and electronic engineering::Computer hardware, software and systems
Online Access:	http://hdl.handle.net/10356/19859
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Institution:	Nanyang Technological University
Language:	English

id	sg-ntu-dr.10356-19859
record_format	dspace
spelling	sg-ntu-dr.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
institution	Nanyang Technological University
building	NTU Library
continent	Asia
country	Singapore Singapore
content_provider	NTU Library
collection	DR-NTU
language	English
topic	DRNTU::Engineering::Electrical and electronic engineering::Computer hardware, software and systems
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
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.
author2	Ho, Hiang Kwee
author_facet	Ho, Hiang Kwee Woon, Kok Meng.
format	Theses and Dissertations
author	Woon, Kok Meng.
author_sort	Woon, Kok Meng.
title	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_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_sort	application of neural network techniques to fault diagnosis of a heat exchanger system
publishDate	2009
url	http://hdl.handle.net/10356/19859
_version_	1761781399003594752

Application of neural network techniques to fault diagnosis of a heat exchanger system

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