A hybrid neural network model for rule generation and its application to process fault detection and diagnosis
In this paper, a hybrid neural network model, based on the integration of fuzzy ARTMAP (FAM) and the rectangular basis function network (RecBFN), which is capable of learning and revealing fuzzy rules is proposed. The hybrid network is able to classify data samples incrementally and, at the same tim...
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| Format: | Article |
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PERGAMON-ELSEVIER SCIENCE LTD
2007
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| Online Access: | http://shdl.mmu.edu.my/3091/ |
| _version_ | 1848790231929585664 |
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| author | TAN, S LIM, C RAO, M |
| author_facet | TAN, S LIM, C RAO, M |
| author_sort | TAN, S |
| building | MMU Institutional Repository |
| collection | Online Access |
| description | In this paper, a hybrid neural network model, based on the integration of fuzzy ARTMAP (FAM) and the rectangular basis function network (RecBFN), which is capable of learning and revealing fuzzy rules is proposed. The hybrid network is able to classify data samples incrementally and, at the same time, to extract rules directly from the network weights for justifying its predictions. With regards to process systems engineering, the proposed network is applied to a fault detection and diagnosis task in a power generation station. Specifically, the efficiency of the network in monitoring the operating conditions of a circulating water (CW) system is evaluated by using a set of real sensor measurements collected from the power station. The rules extracted are analyzed, discussed, and compared with those from a rule extraction method of FAM. From the comparison results, it is observed that the proposed network is able to extract more meaningful rules with a lower degree of rule redundancy and higher interpretability within the neural network framework. The extracted rules are also in agreement with experts' opinions for maintaining the CW system in the power generation plant. (C) 2006 Elsevier Ltd. All rights reserved. |
| first_indexed | 2025-11-14T18:09:20Z |
| format | Article |
| id | mmu-3091 |
| institution | Multimedia University |
| institution_category | Local University |
| last_indexed | 2025-11-14T18:09:20Z |
| publishDate | 2007 |
| publisher | PERGAMON-ELSEVIER SCIENCE LTD |
| recordtype | eprints |
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| spelling | mmu-30912011-09-29T02:11:50Z http://shdl.mmu.edu.my/3091/ A hybrid neural network model for rule generation and its application to process fault detection and diagnosis TAN, S LIM, C RAO, M T Technology (General) QA75.5-76.95 Electronic computers. Computer science In this paper, a hybrid neural network model, based on the integration of fuzzy ARTMAP (FAM) and the rectangular basis function network (RecBFN), which is capable of learning and revealing fuzzy rules is proposed. The hybrid network is able to classify data samples incrementally and, at the same time, to extract rules directly from the network weights for justifying its predictions. With regards to process systems engineering, the proposed network is applied to a fault detection and diagnosis task in a power generation station. Specifically, the efficiency of the network in monitoring the operating conditions of a circulating water (CW) system is evaluated by using a set of real sensor measurements collected from the power station. The rules extracted are analyzed, discussed, and compared with those from a rule extraction method of FAM. From the comparison results, it is observed that the proposed network is able to extract more meaningful rules with a lower degree of rule redundancy and higher interpretability within the neural network framework. The extracted rules are also in agreement with experts' opinions for maintaining the CW system in the power generation plant. (C) 2006 Elsevier Ltd. All rights reserved. PERGAMON-ELSEVIER SCIENCE LTD 2007-03 Article NonPeerReviewed TAN, S and LIM, C and RAO, M (2007) A hybrid neural network model for rule generation and its application to process fault detection and diagnosis. Engineering Applications of Artificial Intelligence, 20 (2). pp. 203-213. ISSN 09521976 http://dx.doi.org/10.1016/j.engappai.2006.06.007 doi:10.1016/j.engappai.2006.06.007 doi:10.1016/j.engappai.2006.06.007 |
| spellingShingle | T Technology (General) QA75.5-76.95 Electronic computers. Computer science TAN, S LIM, C RAO, M A hybrid neural network model for rule generation and its application to process fault detection and diagnosis |
| title | A hybrid neural network model for rule generation and its application to process fault detection and diagnosis |
| title_full | A hybrid neural network model for rule generation and its application to process fault detection and diagnosis |
| title_fullStr | A hybrid neural network model for rule generation and its application to process fault detection and diagnosis |
| title_full_unstemmed | A hybrid neural network model for rule generation and its application to process fault detection and diagnosis |
| title_short | A hybrid neural network model for rule generation and its application to process fault detection and diagnosis |
| title_sort | hybrid neural network model for rule generation and its application to process fault detection and diagnosis |
| topic | T Technology (General) QA75.5-76.95 Electronic computers. Computer science |
| url | http://shdl.mmu.edu.my/3091/ http://shdl.mmu.edu.my/3091/ http://shdl.mmu.edu.my/3091/ |