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dc.contributor.authorChen, Kai-Yingen_US
dc.contributor.authorChen, Long-Shengen_US
dc.contributor.authorChen, Mu-Chenen_US
dc.contributor.authorLee, Chia-Lungen_US
dc.date.accessioned2014-12-08T15:38:05Z-
dc.date.available2014-12-08T15:38:05Z-
dc.date.issued2011-01-01en_US
dc.identifier.issn0166-3615en_US
dc.identifier.urihttp://dx.doi.org/10.1016/j.compind.2010.05.013en_US
dc.identifier.urihttp://hdl.handle.net/11536/26124-
dc.description.abstractDue to the growing demand on electricity, how to improve the efficiency of equipment in a thermal power plant has become one of the critical issues. Reports indicate that efficiency and availability are heavily dependant upon high reliability and maintainability. Recently, the concept of e-maintenance has been introduced to reduce the cost of maintenance. In e-maintenance systems, the intelligent fault detection system plays a crucial role for identifying failures. Data mining techniques are at the core of such intelligent systems and can greatly influence their performance. Applying these techniques to fault detection makes it possible to shorten shutdown maintenance and thus increase the capacity utilization rates of equipment. Therefore, this work proposes a support vector machines (SVM) based model which integrates a dimension reduction scheme to analyze the failures of turbines in thermal power facilities. Finally, a real case from a thermal power plant is provided to evaluate the effectiveness of the proposed SVM based model. Experimental results show that SVM outperforms linear discriminant analysis (LDA) and back-propagation neural networks (BPN) in classification performance. (C) 2010 Elsevier B.V. All rights reserved.en_US
dc.language.isoen_USen_US
dc.subjectThermal poweren_US
dc.subjectMaintenanceen_US
dc.subjectData miningen_US
dc.subjectSupport vector machinesen_US
dc.subjectClassificationen_US
dc.titleUsing SVM based method for equipment fault detection in a thermal power planten_US
dc.typeArticleen_US
dc.identifier.doi10.1016/j.compind.2010.05.013en_US
dc.identifier.journalCOMPUTERS IN INDUSTRYen_US
dc.citation.volume62en_US
dc.citation.issue1en_US
dc.citation.spage42en_US
dc.citation.epage50en_US
dc.contributor.department運輸與物流管理系 註:原交通所+運管所zh_TW
dc.contributor.departmentDepartment of Transportation and Logistics Managementen_US
dc.identifier.wosnumberWOS:000286088800005-
dc.citation.woscount14-
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