Citation
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
Abstract
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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Official URL or Download Paper: http://www.ku.edu.np/kuset/index.php?go=vol6_no2
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Additional Metadata
| Item Type: | Article |
|---|---|
| Divisions: | Faculty of Engineering |
| Publisher: | Kathmandu University |
| Keywords: | Fault detection and diagnosis; Neural network; CSTR |
| Depositing User: | Nabilah Mustapa |
| Date Deposited: | 09 Aug 2015 00:33 |
| Last Modified: | 22 Oct 2015 03:48 |
| URI: | http://psasir.upm.edu.my/id/eprint/14734 |
| Statistic Details: | View Download Statistic |
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