Publication:
Coal-fired boiler fault prediction using artificial neural networks

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dc.citedby4
dc.contributor.authorNistah N.N.M.en_US
dc.contributor.authorLim K.H.en_US
dc.contributor.authorGopal L.en_US
dc.contributor.authorAlnaimi F.B.I.en_US
dc.contributor.authorid57211211943en_US
dc.contributor.authorid25031784300en_US
dc.contributor.authorid26967678300en_US
dc.contributor.authorid58027086700en_US
dc.date.accessioned2023-05-29T06:51:23Z
dc.date.available2023-05-29T06:51:23Z
dc.date.issued2018
dc.description.abstractBoiler 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. Copyright � 2018 Institute of Advanced Engineering and Science. All rights reserved.en_US
dc.description.natureFinalen_US
dc.identifier.doi10.11591/ijece.v8i4.pp2486-2493
dc.identifier.epage2493
dc.identifier.issue4
dc.identifier.scopus2-s2.0-85049557496
dc.identifier.spage2486
dc.identifier.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85049557496&doi=10.11591%2fijece.v8i4.pp2486-2493&partnerID=40&md5=e5d5a7748c1121c14cb8475929476b2e
dc.identifier.urihttps://irepository.uniten.edu.my/handle/123456789/23737
dc.identifier.volume8
dc.publisherInstitute of Advanced Engineering and Scienceen_US
dc.relation.ispartofAll Open Access, Hybrid Gold, Green
dc.sourceScopus
dc.sourcetitleInternational Journal of Electrical and Computer Engineering
dc.titleCoal-fired boiler fault prediction using artificial neural networksen_US
dc.typeArticleen_US
dspace.entity.typePublication
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