Cart (Loading....) | Create Account
Close category search window
 

Intelligent fault diagnosis of rolling bearing based on optimized complementary capability features and RBF neural network by using the Bees Algorithm

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$31 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

4 Author(s)
Attaran, B. ; Mech. Eng. Dept., Shahid Chamran Univ., Ahvaz, Iran ; Ghanbarzadeh, A. ; Zaeri, R. ; Moradi, S.

Rolling element bearings are critical mechanical components in rotating machinery. Fault detection and diagnosis in the early stages of damage are necessary to prevent their malfunctioning and failure during operation. Vibration monitoring is the most widely used and cost-effective monitoring technique to detect, locate and distinguish faults in rolling element bearings. In this paper, an efficient method is proposed to extract optimizing features. The method employs capability features as well as the Bees Algorithm to obtain faults detection accurately and separably. This work presents an algorithm using optimum radial basis neural network by the use of the Bees Algorithm for automated diagnosis of localized faults in rolling element bearings. Optimum complementary capability values extracted from time-domain vibration signals are used as input features for the neural network. Optimum radial basis trained neural network are able to classify different states of the bearing with 100% accuracy. The proposed procedure requires only a few input features. Effectiveness of the proposed method is illustrated using the bearing vibration data obtained experimentally.

Published in:

Control, Instrumentation and Automation (ICCIA), 2011 2nd International Conference on

Date of Conference:

27-29 Dec. 2011

Need Help?


IEEE Advancing Technology for Humanity About IEEE Xplore | Contact | Help | Terms of Use | Nondiscrimination Policy | Site Map | Privacy & Opting Out of Cookies

A not-for-profit organization, IEEE is the world's largest professional association for the advancement of technology.
© Copyright 2014 IEEE - All rights reserved. Use of this web site signifies your agreement to the terms and conditions.