Detection and diagnosis of induction motor faults by intelligent techniques
Other Title(s)
كشف و تشخيص أعطال المحركات الحثية بواسطة التقنيات الذكية
Joint Authors
Kitir, Riyah Najim
Izz al-Din, Muhammad Munis
al-Mashhadani, Yusuf Ismail
Salim, Fuad Latif
Source
Issue
Vol. 23, Issue 1 (31 Jan. 2017), pp.29-47, 19 p.
Publisher
University of Baghdad College of Engineering
Publication Date
2017-01-31
Country of Publication
Iraq
No. of Pages
19
Main Subjects
Electronic engineering
Information Technology and Computer Science
Topics
Abstract EN
This paper presents a complete design and implementation of a monitoring system for the operation of the three-phase induction motors.
This system is built using a personal computer and two types of sensors (current, vibration) to detect some of the mechanical faults that may occur in the motor.
The study and examination of several types of faults including (ball bearing and shaft misalignment faults) have been done through the extraction of fault data by using fast Fourier transform (FFT) technique.
Results showed that the motor current signature analysis (MCSA) technique, and measurement of vibration technique have high possibility in the detection and diagnosis of most mechanical faults with high accuracy.
Subsequently, diagnosis system is developed to determine the status of the motor without the need for an expert.
This system is based on artificial neural network (ANN) and it is characterized by speed and accuracy and the ability to detect more than one fault at the same time.
American Psychological Association (APA)
Kitir, Riyah Najim& Izz al-Din, Muhammad Munis& al-Mashhadani, Yusuf Ismail& Salim, Fuad Latif. 2017. Detection and diagnosis of induction motor faults by intelligent techniques. Journal of Engineering،Vol. 23, no. 1, pp.29-47.
https://search.emarefa.net/detail/BIM-746530
Modern Language Association (MLA)
Kitir, Riyah Najim…[et al.]. Detection and diagnosis of induction motor faults by intelligent techniques. Journal of Engineering Vol. 23, no. 1 (Jan. 2017), pp.29-47.
https://search.emarefa.net/detail/BIM-746530
American Medical Association (AMA)
Kitir, Riyah Najim& Izz al-Din, Muhammad Munis& al-Mashhadani, Yusuf Ismail& Salim, Fuad Latif. Detection and diagnosis of induction motor faults by intelligent techniques. Journal of Engineering. 2017. Vol. 23, no. 1, pp.29-47.
https://search.emarefa.net/detail/BIM-746530
Data Type
Journal Articles
Language
English
Notes
Includes appendices : p. 41-47
Record ID
BIM-746530