Process fault detection using hierarchical artificial neural network diagnostic strategy
This paper focuses on the use of artificial neural network (ANN) to detect and diagnose fault in process plant. In this work, the ANN uses two layers of hierarchical diagnostic strategy. The first layer diagnoses the node where the fault originated and the second layer classifies the type of faults...
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| Format: | Article |
| Language: | English English |
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Penerbit UTM Press
2007
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| Online Access: | http://eprints.utm.my/8104/ http://eprints.utm.my/8104/3/MohamadWijayanuddinAli2007_ProcessFaultDetectionUsingHierarchicalArtificial.pdf http://eprints.utm.my/8104/4/291 |
| _version_ | 1848891617008680960 |
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| author | Othman, Mahamad Rizza Ali, Mohamad Wijayanuddin Kamsah, Mohd. Zaki |
| author_facet | Othman, Mahamad Rizza Ali, Mohamad Wijayanuddin Kamsah, Mohd. Zaki |
| author_sort | Othman, Mahamad Rizza |
| building | UTeM Institutional Repository |
| collection | Online Access |
| description | This paper focuses on the use of artificial neural network (ANN) to detect and diagnose fault in process plant. In this work, the ANN uses two layers of hierarchical diagnostic strategy. The first layer diagnoses the node where the fault originated and the second layer classifies the type of faults or malfunctions occurred on that particular node. The architecture of the ANN model is founded on a multilayer feed forward network and used back propagation algorithm as the training scheme. In order to find the most suitable configuration of ANN, a topology analysis is conducted. The effectiveness of the method is demonstrated by using a fatty acid fractionation column. Results show that the system is successful in detecting original single and transient fault introduced within the process plant model. |
| first_indexed | 2025-11-15T21:00:48Z |
| format | Article |
| id | utm-8104 |
| institution | Universiti Teknologi Malaysia |
| institution_category | Local University |
| language | English English |
| last_indexed | 2025-11-15T21:00:48Z |
| publishDate | 2007 |
| publisher | Penerbit UTM Press |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | utm-81042017-11-01T04:17:25Z http://eprints.utm.my/8104/ Process fault detection using hierarchical artificial neural network diagnostic strategy Othman, Mahamad Rizza Ali, Mohamad Wijayanuddin Kamsah, Mohd. Zaki TP Chemical technology This paper focuses on the use of artificial neural network (ANN) to detect and diagnose fault in process plant. In this work, the ANN uses two layers of hierarchical diagnostic strategy. The first layer diagnoses the node where the fault originated and the second layer classifies the type of faults or malfunctions occurred on that particular node. The architecture of the ANN model is founded on a multilayer feed forward network and used back propagation algorithm as the training scheme. In order to find the most suitable configuration of ANN, a topology analysis is conducted. The effectiveness of the method is demonstrated by using a fatty acid fractionation column. Results show that the system is successful in detecting original single and transient fault introduced within the process plant model. Penerbit UTM Press 2007-11-26 Article PeerReviewed application/pdf en http://eprints.utm.my/8104/3/MohamadWijayanuddinAli2007_ProcessFaultDetectionUsingHierarchicalArtificial.pdf text/html en http://eprints.utm.my/8104/4/291 Othman, Mahamad Rizza and Ali, Mohamad Wijayanuddin and Kamsah, Mohd. Zaki (2007) Process fault detection using hierarchical artificial neural network diagnostic strategy. Jurnal Teknologi (46F). pp. 11-26. ISSN 2180-3722 DOI:10.11113/jt.v46.301 |
| spellingShingle | TP Chemical technology Othman, Mahamad Rizza Ali, Mohamad Wijayanuddin Kamsah, Mohd. Zaki Process fault detection using hierarchical artificial neural network diagnostic strategy |
| title | Process fault detection using hierarchical artificial neural network diagnostic strategy
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| title_full | Process fault detection using hierarchical artificial neural network diagnostic strategy
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| title_fullStr | Process fault detection using hierarchical artificial neural network diagnostic strategy
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| title_full_unstemmed | Process fault detection using hierarchical artificial neural network diagnostic strategy
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| title_short | Process fault detection using hierarchical artificial neural network diagnostic strategy
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| title_sort | process fault detection using hierarchical artificial neural network diagnostic strategy |
| topic | TP Chemical technology |
| url | http://eprints.utm.my/8104/ http://eprints.utm.my/8104/ http://eprints.utm.my/8104/3/MohamadWijayanuddinAli2007_ProcessFaultDetectionUsingHierarchicalArtificial.pdf http://eprints.utm.my/8104/4/291 |