Vibration-based fault diagnosis of a rotor bearing system using artificial neural network and support vector machine Online publication date: Sat, 29-Nov-2014
by Pavan Kumar Kankar; Satish C. Sharma; Suraj Prakash Harsha
International Journal of Modelling, Identification and Control (IJMIC), Vol. 15, No. 3, 2012
Abstract: This paper presents the vibration analysis of the healthy and cracked rotor supported on various faulty bearings. In rotating machines, one of the main causes of breakdown is faults in ball bearings. This study is mainly focused on fault diagnosis of rotor bearing system using artificial neural network (ANN) and support vector machine (SVM). The vibration response is obtained and analysed for the healthy and cracked rotor with the various defects of ball bearings. The specific defects considered on bearings are: crack in outer race, inner race with spall and corrosion pitting in balls. Statistical methods are used to extract features and to reduce the dimensionality of original vibration features. A comparative experimental study of the effectiveness of ANN and SVM is carried out. The results show that for this study, ANN is a better classifier than SVM.
Existing subscribers:
Go to Inderscience Online Journals to access the Full Text of this article.
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 Modelling, Identification and Control (IJMIC):
Login with your Inderscience username and 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
Our Blog 
Follow us on Twitter 
Visit us on Facebook