NDLI: Fault detection of rolling element bearing based on principal component analysis

Content Provider	IEEE Xplore Digital Library
Author	Liying Jiang Xinxin Fu Jianguo Cui Zhonghai Li
Copyright Year	2012
Description	Author affiliation: School of Automation, Shenyang Aerospace University, 110136, China (Liying Jiang; Xinxin Fu; Jianguo Cui; Zhonghai Li)
Abstract	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.
Starting Page	2944
Ending Page	2948
File Size	495368
Page Count	5
File Format	PDF
ISBN	9781457720734
e-ISBN	9781457720741
DOI	10.1109/CCDC.2012.6243071
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2012-05-23
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Vibrations Fault Detection Vibration Signals Fault detection Rolling Bearing Rolling bearings Hidden Markov models Vectors Monitoring Principal component analysis PCA
Content Type	Text
Resource Type	Article

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