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
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author Abdul Rahman, Ribhan Zafira
Che Soh, Azura
Muhammad, Noor Fadzlina
author_facet Abdul Rahman, Ribhan Zafira
Che Soh, Azura
Muhammad, Noor Fadzlina
author_sort Abdul Rahman, Ribhan Zafira
collection UPM
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.
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spelling 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
spellingShingle Abdul Rahman, Ribhan Zafira
Che Soh, Azura
Muhammad, Noor Fadzlina
Fault detection and diagnosis for continuous stirred tank reactor using neural network
title 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_short 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
url http://psasir.upm.edu.my/id/eprint/14734/1/Fault%20detection%20and%20diagnosis%20for%20continuous%20stirred%20tank%20reactor%20using%20neural%20network.pdf
work_keys_str_mv AT abdulrahmanribhanzafira faultdetectionanddiagnosisforcontinuousstirredtankreactorusingneuralnetwork
AT chesohazura faultdetectionanddiagnosisforcontinuousstirredtankreactorusingneuralnetwork
AT muhammadnoorfadzlina faultdetectionanddiagnosisforcontinuousstirredtankreactorusingneuralnetwork