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Fault detection and diagnosis for continuous stirred tank reactor using neural network

The paper focuses on the application of neural network techniques in fault detection and diagnosis. The objective of this paper is to detect and diagnose the faults to a continuous stirred tank reactor (CSTR). Fault detection is performed by using the error signals, where when error signal is zero o...

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Main Authors: Abdul Rahman, Ribhan Zafira, Che Soh, Azura, Muhammad, Noor Fadzlina
Format: Article
Language:English
Published: Kathmandu University 2010
Online Access:http://psasir.upm.edu.my/id/eprint/14734/1/Fault%20detection%20and%20diagnosis%20for%20continuous%20stirred%20tank%20reactor%20using%20neural%20network.pdf
http://psasir.upm.edu.my/id/eprint/14734/
http://www.ku.edu.np/kuset/index.php?go=vol6_no2
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spelling my.upm.eprints.147342015-10-22T03:48:08Z http://psasir.upm.edu.my/id/eprint/14734/ Fault detection and diagnosis for continuous stirred tank reactor using neural network Abdul Rahman, Ribhan Zafira Che Soh, Azura Muhammad, Noor Fadzlina The paper focuses on the application of neural network techniques in fault detection and diagnosis. The objective of this paper is to detect and diagnose the faults to a continuous stirred tank reactor (CSTR). Fault detection is performed by using the error signals, where when error signal is zero or nearly zero, the system is in normal condition, and when the fault occurs, error signals should distinctively diverge from zero. The fault diagnosis is performed by identifying the amplitude error of the CSTR output error. Kathmandu University 2010-11 Article PeerReviewed application/pdf en http://psasir.upm.edu.my/id/eprint/14734/1/Fault%20detection%20and%20diagnosis%20for%20continuous%20stirred%20tank%20reactor%20using%20neural%20network.pdf Abdul Rahman, Ribhan Zafira and Che Soh, Azura and Muhammad, Noor Fadzlina (2010) Fault detection and diagnosis for continuous stirred tank reactor using neural network. Kathmandu University Journal of Science, Engineering and Technology, 6 (2). pp. 66-74. ISSN 1816-8752 http://www.ku.edu.np/kuset/index.php?go=vol6_no2
institution Universiti Putra Malaysia
building UPM Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Putra Malaysia
content_source UPM Institutional Repository
url_provider http://psasir.upm.edu.my/
language English
description The paper focuses on the application of neural network techniques in fault detection and diagnosis. The objective of this paper is to detect and diagnose the faults to a continuous stirred tank reactor (CSTR). Fault detection is performed by using the error signals, where when error signal is zero or nearly zero, the system is in normal condition, and when the fault occurs, error signals should distinctively diverge from zero. The fault diagnosis is performed by identifying the amplitude error of the CSTR output error.
format Article
author Abdul Rahman, Ribhan Zafira
Che Soh, Azura
Muhammad, Noor Fadzlina
spellingShingle Abdul Rahman, Ribhan Zafira
Che Soh, Azura
Muhammad, Noor Fadzlina
Fault detection and diagnosis for continuous stirred tank reactor using neural network
author_facet Abdul Rahman, Ribhan Zafira
Che Soh, Azura
Muhammad, Noor Fadzlina
author_sort Abdul Rahman, Ribhan Zafira
title Fault detection and diagnosis for continuous stirred tank reactor using neural network
title_short Fault detection and diagnosis for continuous stirred tank reactor using neural network
title_full Fault detection and diagnosis for continuous stirred tank reactor using neural network
title_fullStr Fault detection and diagnosis for continuous stirred tank reactor using neural network
title_full_unstemmed Fault detection and diagnosis for continuous stirred tank reactor using neural network
title_sort fault detection and diagnosis for continuous stirred tank reactor using neural network
publisher Kathmandu University
publishDate 2010
url http://psasir.upm.edu.my/id/eprint/14734/1/Fault%20detection%20and%20diagnosis%20for%20continuous%20stirred%20tank%20reactor%20using%20neural%20network.pdf
http://psasir.upm.edu.my/id/eprint/14734/
http://www.ku.edu.np/kuset/index.php?go=vol6_no2
_version_ 1643825722877280256
score 13.211869

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