Coal-fired boiler fault prediction using artificial neural networks
Copyright © 2018 Institute of Advanced Engineering and Science. All rights reserved. Boiler fault is a critical issue in a coal-fired power plant due to its high temperature and high pressure characteristics. The complexity of boiler design increases the difficulty of fault investigation in a quick...
| Main Authors: | , , , |
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| Format: | Journal Article |
| Published: |
Pergamon Press plc
2018
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| Online Access: | http://hdl.handle.net/20.500.11937/71520 |
| _version_ | 1848762502030032896 |
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| author | Nistah, N. Lim, Hann Gopal, L. Alnaimi, F. |
| author_facet | Nistah, N. Lim, Hann Gopal, L. Alnaimi, F. |
| author_sort | Nistah, N. |
| building | Curtin Institutional Repository |
| collection | Online Access |
| description | Copyright © 2018 Institute of Advanced Engineering and Science. All rights reserved. Boiler fault is a critical issue in a coal-fired power plant due to its high temperature and high pressure characteristics. The complexity of boiler design increases the difficulty of fault investigation in a quick moment to avoid long duration shut-down. In this paper, a boiler fault prediction model is proposed using artificial neural network. The key influential parameters analysis is carried out to identify its correlation with the performance of the boiler. The prediction model is developed to achieve the least misclassification rate and mean squared error. Artificial neural network is trained using a set of boiler operational parameters. Subsequenlty, the trained model is used to validate its prediction accuracy against actual fault value from a collected real plant data. With reference to the study and test results, two set of initial weights have been tested to verify the repeatability of the correct prediction. The results show that the artificial neural network implemented is able to provide an average of above 92% prediction rate of accuracy. |
| first_indexed | 2025-11-14T10:48:35Z |
| format | Journal Article |
| id | curtin-20.500.11937-71520 |
| institution | Curtin University Malaysia |
| institution_category | Local University |
| last_indexed | 2025-11-14T10:48:35Z |
| publishDate | 2018 |
| publisher | Pergamon Press plc |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | curtin-20.500.11937-715202018-12-13T09:34:50Z Coal-fired boiler fault prediction using artificial neural networks Nistah, N. Lim, Hann Gopal, L. Alnaimi, F. Copyright © 2018 Institute of Advanced Engineering and Science. All rights reserved. Boiler fault is a critical issue in a coal-fired power plant due to its high temperature and high pressure characteristics. The complexity of boiler design increases the difficulty of fault investigation in a quick moment to avoid long duration shut-down. In this paper, a boiler fault prediction model is proposed using artificial neural network. The key influential parameters analysis is carried out to identify its correlation with the performance of the boiler. The prediction model is developed to achieve the least misclassification rate and mean squared error. Artificial neural network is trained using a set of boiler operational parameters. Subsequenlty, the trained model is used to validate its prediction accuracy against actual fault value from a collected real plant data. With reference to the study and test results, two set of initial weights have been tested to verify the repeatability of the correct prediction. The results show that the artificial neural network implemented is able to provide an average of above 92% prediction rate of accuracy. 2018 Journal Article http://hdl.handle.net/20.500.11937/71520 10.11591/ijece.v8i4.pp2486-2493 Pergamon Press plc restricted |
| spellingShingle | Nistah, N. Lim, Hann Gopal, L. Alnaimi, F. Coal-fired boiler fault prediction using artificial neural networks |
| title | Coal-fired boiler fault prediction using artificial neural networks |
| title_full | Coal-fired boiler fault prediction using artificial neural networks |
| title_fullStr | Coal-fired boiler fault prediction using artificial neural networks |
| title_full_unstemmed | Coal-fired boiler fault prediction using artificial neural networks |
| title_short | Coal-fired boiler fault prediction using artificial neural networks |
| title_sort | coal-fired boiler fault prediction using artificial neural networks |
| url | http://hdl.handle.net/20.500.11937/71520 |