An intelligent automated method to diagnose and segregate induction motor faults
Joint Authors
Shaykh, Muhammad Aman
Nur, Nursyarizal Muhd
Ibrahim, Tayyib
Bakhsh, Shaykh Tahir
Sad, Nur al-Din B.
Irfan, M.
Source
Issue
Vol. 13, Issue 2 (30 Jun. 2017), pp.241-254, 14 p.
Publisher
Publication Date
2017-06-30
Country of Publication
Algeria
No. of Pages
14
Main Subjects
Topics
Abstract EN
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.
American Psychological Association (APA)
Shaykh, Muhammad Aman& Nur, Nursyarizal Muhd& Ibrahim, Tayyib& Bakhsh, Shaykh Tahir& Irfan, M.& Sad, Nur al-Din B.. 2017. An intelligent automated method to diagnose and segregate induction motor faults. Journal of Electrical Systems،Vol. 13, no. 2, pp.241-254.
https://search.emarefa.net/detail/BIM-748148
Modern Language Association (MLA)
Shaykh, Muhammad Aman…[et al.]. An intelligent automated method to diagnose and segregate induction motor faults. Journal of Electrical Systems Vol. 13, no. 2 (2017), pp.241-254.
https://search.emarefa.net/detail/BIM-748148
American Medical Association (AMA)
Shaykh, Muhammad Aman& Nur, Nursyarizal Muhd& Ibrahim, Tayyib& Bakhsh, Shaykh Tahir& Irfan, M.& Sad, Nur al-Din B.. An intelligent automated method to diagnose and segregate induction motor faults. Journal of Electrical Systems. 2017. Vol. 13, no. 2, pp.241-254.
https://search.emarefa.net/detail/BIM-748148
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references : p. 253-254
Record ID
BIM-748148