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Fault classification system for switchgear cbm from an ultrasound analysis technique using extreme learning machine

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dc.citedby2
dc.contributor.authorIshak S.en_US
dc.contributor.authorYaw C.T.en_US
dc.contributor.authorKoh S.P.en_US
dc.contributor.authorTiong S.K.en_US
dc.contributor.authorChen C.P.en_US
dc.contributor.authorYusaf T.en_US
dc.contributor.authorid57194057526en_US
dc.contributor.authorid36560884300en_US
dc.contributor.authorid22951210700en_US
dc.contributor.authorid15128307800en_US
dc.contributor.authorid25824552100en_US
dc.contributor.authorid23112065900en_US
dc.date.accessioned2023-05-29T09:05:49Z
dc.date.available2023-05-29T09:05:49Z
dc.date.issued2021
dc.descriptionClassification (of information); Computer aided diagnosis; Decision making; Graphical user interfaces; Knowledge acquisition; Machine learning; Maintenance; Neural networks; Ultrasonic applications; Analysis techniques; Classification system; Condition; Condition based maintenance; Decisions makings; Diagnostic tests; Fault classification; Faults diagnosis; Training process; Ultrasound dataen_US
dc.description.abstractCurrently, the existing condition-based maintenance (CBM) diagnostic test practices for ultrasound require the tester to interpret test results manually. Different testers may give different opinions or interpretations of the detected ultrasound. It leads to wrong interpretation due to depending on tester experience. Furthermore, there is no commercially available product to standardize the interpretation of the ultrasound data. Therefore, the objective is the correct interpretation of an ultrasound, which is one of the CBM methods for medium switchgears, by using an artificial neural network (ANN), to give more accurate results when assessing their condition. Information and test results from various switchgears were gathered in order to develop the classification and severity of the corona, surface discharge, and arcing inside of the switchgear. The ultrasound data were segregated based on their defects found during maintenance. In total, 314 cases of normal, 160 cases of the corona, 149 cases of tracking, and 203 cases of arcing were collected. Noise from ultrasound data was removed before uploading it as a training process to the ANN engine, which used the extreme learning machine (ELM) model. The developed AI-based switchgear faults classification system was designed and incorporated with the feature of scalability and can be tested and replicated for other switchgear conditions. A customized graphical user interface (GUI), Ultrasound Analyzer System (UAS), was also developed, to enable users to obtain the switchgear condition or classification output via a graphical interface screen. Hence, accurate decision-making based on this analysis can be made to prioritize the urgency for the remedial works. � 2021 by the authors. Licensee MDPI, Basel, Switzerland.en_US
dc.description.natureFinalen_US
dc.identifier.ArtNo6279
dc.identifier.doi10.3390/en14196279
dc.identifier.issue19
dc.identifier.scopus2-s2.0-85116464830
dc.identifier.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85116464830&doi=10.3390%2fen14196279&partnerID=40&md5=0d31e2fddb79750946f68ebd42f318fb
dc.identifier.urihttps://irepository.uniten.edu.my/handle/123456789/25969
dc.identifier.volume14
dc.publisherMDPIen_US
dc.relation.ispartofAll Open Access, Gold
dc.sourceScopus
dc.sourcetitleEnergies
dc.titleFault classification system for switchgear cbm from an ultrasound analysis technique using extreme learning machineen_US
dc.typeArticleen_US
dspace.entity.typePublication
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