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

Classification (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...

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Main Authors: Ishak S., Yaw C.T., Koh S.P., Tiong S.K., Chen C.P., Yusaf T.
Other Authors: 57194057526
Format: Article
Published: MDPI 2023
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spelling my.uniten.dspace-259692023-05-29T17:05:49Z Fault classification system for switchgear cbm from an ultrasound analysis technique using extreme learning machine Ishak S. Yaw C.T. Koh S.P. Tiong S.K. Chen C.P. Yusaf T. 57194057526 36560884300 22951210700 15128307800 25824552100 23112065900 Classification (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 data Currently, 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. Final 2023-05-29T09:05:49Z 2023-05-29T09:05:49Z 2021 Article 10.3390/en14196279 2-s2.0-85116464830 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85116464830&doi=10.3390%2fen14196279&partnerID=40&md5=0d31e2fddb79750946f68ebd42f318fb https://irepository.uniten.edu.my/handle/123456789/25969 14 19 6279 All Open Access, Gold MDPI Scopus
institution Universiti Tenaga Nasional
building UNITEN Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Tenaga Nasional
content_source UNITEN Institutional Repository
url_provider http://dspace.uniten.edu.my/
description Classification (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 data
author2 57194057526
author_facet 57194057526
Ishak S.
Yaw C.T.
Koh S.P.
Tiong S.K.
Chen C.P.
Yusaf T.
format Article
author Ishak S.
Yaw C.T.
Koh S.P.
Tiong S.K.
Chen C.P.
Yusaf T.
spellingShingle Ishak S.
Yaw C.T.
Koh S.P.
Tiong S.K.
Chen C.P.
Yusaf T.
Fault classification system for switchgear cbm from an ultrasound analysis technique using extreme learning machine
author_sort Ishak S.
title Fault classification system for switchgear cbm from an ultrasound analysis technique using extreme learning machine
title_short Fault classification system for switchgear cbm from an ultrasound analysis technique using extreme learning machine
title_full Fault classification system for switchgear cbm from an ultrasound analysis technique using extreme learning machine
title_fullStr Fault classification system for switchgear cbm from an ultrasound analysis technique using extreme learning machine
title_full_unstemmed Fault classification system for switchgear cbm from an ultrasound analysis technique using extreme learning machine
title_sort fault classification system for switchgear cbm from an ultrasound analysis technique using extreme learning machine
publisher MDPI
publishDate 2023
_version_ 1806426302235803648
score 13.222552

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