Detection of induction motor broken rotor bar faults under no load condition by using support vector machines
by Hayri Arabacı; Mohamed Ali Mohamed
International Journal of Intelligent Engineering Informatics (IJIEI), Vol. 9, No. 5, 2021

Abstract: An important fault in induction motor is the broken rotor bar. Many techniques have been proposed for the detection of the rotor fault. However, the traditional techniques like motor current signature analysis have difficulty in detecting the rotor faults at 'no load' condition due to low slip. In this study, an algorithm which uses fast Fourier transform, principal component analysis and intelligent classifiers is proposed. The proposed algorithm was able to accurately detect the rotor faults of different severity levels at low slip. Experiments were carried out with three submersible induction motors. Four different rotor faults and healthy motor conditions were investigated for each motor. The motors were loaded different load levels to test the proposed algorithm. The best results were achieved with medium Gaussian support vector machine. The condition of having any faulted bar in the motor was obtained with 100% accuracy. Faults classification carried out by 92.2% accuracy.

Online publication date: Thu, 03-Feb-2022

The full text of this article is only available to individual subscribers or to users at subscribing institutions.

 
Existing subscribers:
Go to Inderscience Online Journals to access the Full Text of this article.

Pay per view:
If you are not a subscriber and you just want to read the full contents of this article, buy online access here.

Complimentary Subscribers, Editors or Members of the Editorial Board of the International Journal of Intelligent Engineering Informatics (IJIEI):
Login with your Inderscience username and password:

    Username:        Password:         

Forgotten your password?


Want to subscribe?
A subscription gives you complete access to all articles in the current issue, as well as to all articles in the previous three years (where applicable). See our Orders page to subscribe.

If you still need assistance, please email subs@inderscience.com