An intelligent automated method to diagnose and segregate induction motor faults

In the last few decades, various methods and alternative techniques have been proposed and implemented to diagnose induction motor faults. In an induction motor, bearing faults account the largest percentage of motor failure. Moreover, the existing techniques related to current and instantaneous pow...

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Main Authors: Sheikh, M.A., Nor, N.M., Ibrahim, T., Bakhsh, S.T., Irfan, M., Saad, N.B.
Format: Article
Institution: Universiti Teknologi Petronas
Record Id / ISBN-0: utp-eprints.19492 /
Published: Engineering and Scientific Research Groups 2017
Online Access: https://www.scopus.com/inward/record.uri?eid=2-s2.0-85020069981&partnerID=40&md5=dab42f66542626c566d5e6f8f943b1c6
http://eprints.utp.edu.my/19492/
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spelling utp-eprints.194922018-04-20T06:02:33Z An intelligent automated method to diagnose and segregate induction motor faults Sheikh, M.A. Nor, N.M. Ibrahim, T. Bakhsh, S.T. Irfan, M. Saad, N.B. In the last few decades, various methods and alternative techniques have been proposed and implemented to diagnose induction motor faults. In an induction motor, bearing faults account the largest percentage of motor failure. Moreover, the existing techniques related to current and instantaneous power analysis are incompatible to diagnose the distributed bearing faults (race roughness), due to the fact that there does not exist any fault characteristics frequency model for these type of faults. In such a condition to diagnose and segregate the severity of fault is a challenging task. Thus, to overcome existing problem an alternative solution based on artificial neural network (ANN) is proposed. The proposed technique is harmonious because it does not oblige any mathematical models and the distributed faults are diagnosed and classified at incipient stage based on the extracted features from Park vector analysis (PVA). Moreover, the experimental results obtained through features of PVA and statistical evaluation of automated method shows the capability of proposed method that it is not only capable enough to diagnose fault but also can segregate bearing distributed defects. © JES 2017. Engineering and Scientific Research Groups 2017 Article PeerReviewed https://www.scopus.com/inward/record.uri?eid=2-s2.0-85020069981&partnerID=40&md5=dab42f66542626c566d5e6f8f943b1c6 Sheikh, M.A. and Nor, N.M. and Ibrahim, T. and Bakhsh, S.T. and Irfan, M. and Saad, N.B. (2017) An intelligent automated method to diagnose and segregate induction motor faults. Journal of Electrical Systems, 13 (2). pp. 241-254. http://eprints.utp.edu.my/19492/
institution Universiti Teknologi Petronas
collection UTP Institutional Repository
description In the last few decades, various methods and alternative techniques have been proposed and implemented to diagnose induction motor faults. In an induction motor, bearing faults account the largest percentage of motor failure. Moreover, the existing techniques related to current and instantaneous power analysis are incompatible to diagnose the distributed bearing faults (race roughness), due to the fact that there does not exist any fault characteristics frequency model for these type of faults. In such a condition to diagnose and segregate the severity of fault is a challenging task. Thus, to overcome existing problem an alternative solution based on artificial neural network (ANN) is proposed. The proposed technique is harmonious because it does not oblige any mathematical models and the distributed faults are diagnosed and classified at incipient stage based on the extracted features from Park vector analysis (PVA). Moreover, the experimental results obtained through features of PVA and statistical evaluation of automated method shows the capability of proposed method that it is not only capable enough to diagnose fault but also can segregate bearing distributed defects. © JES 2017.
format Article
author Sheikh, M.A.
Nor, N.M.
Ibrahim, T.
Bakhsh, S.T.
Irfan, M.
Saad, N.B.
spellingShingle Sheikh, M.A.
Nor, N.M.
Ibrahim, T.
Bakhsh, S.T.
Irfan, M.
Saad, N.B.
An intelligent automated method to diagnose and segregate induction motor faults
author_sort Sheikh, M.A.
title An intelligent automated method to diagnose and segregate induction motor faults
title_short An intelligent automated method to diagnose and segregate induction motor faults
title_full An intelligent automated method to diagnose and segregate induction motor faults
title_fullStr An intelligent automated method to diagnose and segregate induction motor faults
title_full_unstemmed An intelligent automated method to diagnose and segregate induction motor faults
title_sort intelligent automated method to diagnose and segregate induction motor faults
publisher Engineering and Scientific Research Groups
publishDate 2017
url https://www.scopus.com/inward/record.uri?eid=2-s2.0-85020069981&partnerID=40&md5=dab42f66542626c566d5e6f8f943b1c6
http://eprints.utp.edu.my/19492/
_version_ 1741196215327916032
score 11.62408

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