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Shock and Vibration
Volume 20, Issue 5, Pages 833-846

Safety Region Estimation and State Identification of Rolling Bearing Based on Statistical Feature Extraction

Yuan Zhang,1,2 Yong Qin,2 Zongyi Xing,3 Limin Jia,2 and Xiaoqing Cheng2

1School of Traffic and Transportation, Beijing Jiaotong University, Beijing, China
2State Key Laboratory of Rail Traffic Control and Safety, Beijing Jiaotong University, Beijing, China
3Department of Automation, Nanjing University of Science and Technology, Nanjing, Jiangsu, China

Received 16 October 2012; Revised 3 January 2013; Accepted 14 March 2013

Copyright © 2013 Hindawi Publishing Corporation. 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.


The idea of safety region was introduced into the rolling bearing condition monitoring. The safety region estimation and the state identification of the rolling bearing operational were performed by the comprehensive utilization of Empirical Mode Decomposition (EMD), Principal Component Analysis (PCA), and the Least Square Support Vector Machine (LSSVM). The collected vibration data was segmented according to a certain time interval, and then the Intrinsic Mode Functions (IMFs) of each piece of the data were obtained by EMD. The control limits of two statistical variables extracted by PCA were presented as state characteristics. The safety region estimation for the rolling bearing operational status was performed by two-class LSSVM. The states of normal bearing, ball fault, inner race fault, and outer race fault were identified by the multi-class LSSVM. The results show that the estimation accuracy for both the safety region and the states identification reached 95%, and that the validity of the proposed method was verified.