Fault Detection of Rolling Element Bearing Based on Principal Component Analysis

摘要：

The Principal Component Analysis (PCA) has been widely used to detect and diagnose the faults of industry processes. However, this technique is rarely applied to the fault detection and diagnosis of rolling element bearing. The main reason is that PCA is a kind of multivariate statistical technology, but bearing vibration signal is one dimensional time series. A new method of fault detection based on PCA for rolling element bearing is proposed in this paper. Firstly, the vibration signal is mapped into a high dimensional space. Then, PCA is applied in this space. The proposed method is tested with experimental data collected from drive end ball bearing of a 2 hp Reliance Electric motor driven mechanical system. The simulation results show the PCA-based method of bearing fault detection is effective and is superior to the traditional PCA-based approach.

关键词： Vibration Signals PCA Rolling Bearing Fault Detection

作者: Liying Jiang Xinxin Fu Jianguo Cui Zhonghai Li

作者单位: School of Automation, Shenyang Aerospace University, Shenyang 110136, China

会议类型: 国际会议

会议名称: The 24th Chinese Control and Decision Conference (第24届中国控制与决策学术年会 2012 CCDC)

会议地点: 太原

会议语种:英文

页码: 2956-2960

在线出版日期: 2012-05-23（万方平台首次上网日期，不代表论文的发表时间）