Table of Contents
ISRN Mechanical Engineering
Volume 2014, Article ID 160281, 4 pages
Research Article

Rolling Bearing Fault Diagnosis Based on Physical Model and One-Class Support Vector Machine

Power Equipment Research Institute Xi’an Aeronautical College, 259 West Second Ring, Xi’an, China

Received 8 October 2013; Accepted 9 December 2013; Published 14 April 2014

Academic Editors: A. Z. Sahin and A. Tounsi

Copyright © 2014 Li Xiangyang and Chen Wanqiang. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


This paper aims at diagnosing the fault of rolling bearings and establishes the system of dynamics model with the consideration of rolling bearing with nonlinear bearing force, the radial clearance, and other nonlinear factors, using Runge-Kutla such as Hertzian elastic contactforce and internal radial clearance, which are solved by the Runge-Kutta method. Using simulated data of the normal state, a self-adaptive alarm method for bearing condition based on one-class support vector machine is proposed. Test samples were diagnosed with a recognition accuracy over 90%. The present method is further applied to the vibration monitoring of rolling bearings. The alarms under the actual abnormal condition meet the demand of bearings monitoring.