Affiliations: School of Traffic and Transportation, Beijing Jiaotong
University, Beijing, China | State Key Laboratory of Rail Traffic Control and
Safety, Beijing Jiaotong University, Beijing, China | Department of Automation, Nanjing University
of Science and Technology, Nanjing, Jiangsu, China
Note: [] Corresponding author: Yong Qin, State Key Laboratory of Rail Traffic Control
and Safety, Beijing Jiaotong University, Beijing, China. Tel.: +86 10 51683973; Fax: +86 10 51684081;
E-mail: [email protected]
Abstract: 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.
Keywords: Rolling bearings, condition monitoring, safety region, empirical mode decomposition, principal component analysis, least square support vector machine
