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Development and implementation of intelligent condition monitoring system for steam turbine trips

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dc.citedby1
dc.contributor.authorAlnaimi F.B.I.en_US
dc.contributor.authorIsmail R.I.B.en_US
dc.contributor.authorKer P.J.en_US
dc.contributor.authorid58027086700en_US
dc.contributor.authorid57189236796en_US
dc.contributor.authorid37461740800en_US
dc.date.accessioned2023-05-29T06:13:22Z
dc.date.available2023-05-29T06:13:22Z
dc.date.issued2016
dc.description.abstractSustainable initiatives are increasingly getting attention from the research community and one of the aspects in achieving sustainable development is to enhance the efficiency and optimize the technology used to generate and utilize energy. Fault detection and diagnosis is a critical optimization factor in power generation sector. Early faults detection ensures that correct mitigation measures can be taken, whilst false alarms should be eschewed to avoid unnecessary cost of operation, interruption and downtime. Pure Intelligent Condition Monitoring System (ICMS) represented by artificial neural network (ANN), developed by training the network with real operational data, may be proven to be useful for realtime monitoring of a power plant. In this work, an integrated data preparation method has been proposed and the development of ANN models to detect steam turbine trip for Malaysia MNJ power station will be presented. Two models adopting feed forward with back propagation ANN were trained with real data from the MNJ station. The developed models were capable of detecting the specific trip within a period of 32 minutes before the actual trip occurrence, which is considered to provide good and satisfactory early fault detection. � 2006-2016 Asian Research Publishing Network (ARPN). All rights reserved.en_US
dc.description.natureFinalen_US
dc.identifier.epage14283
dc.identifier.issue24
dc.identifier.scopus2-s2.0-85009160454
dc.identifier.spage14275
dc.identifier.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85009160454&partnerID=40&md5=3da3f8a8c6d986223d34717b4cd4fb0b
dc.identifier.urihttps://irepository.uniten.edu.my/handle/123456789/22915
dc.identifier.volume11
dc.publisherAsian Research Publishing Networken_US
dc.sourceScopus
dc.sourcetitleARPN Journal of Engineering and Applied Sciences
dc.titleDevelopment and implementation of intelligent condition monitoring system for steam turbine tripsen_US
dc.typeArticleen_US
dspace.entity.typePublication
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