An effective approach to rolling bearing diagnosis based on Adaptive Redundant Second-Generation Wavelet
by Huaxin Chen, Xuefeng Chen, Yanyang Zi, Feng Ding, Hongrui Cao, Jiyong Tan, Hongkai Jiang, Zhengjia He
International Journal of Materials and Product Technology (IJMPT), Vol. 33, No. 1/2, 2008

Abstract: De-noising and extraction of weak signals are crucial to fault prognostics, and the wavelet transform has been widely used in signal de-noising. In this paper, a new method, which combines the Adaptive Redundant Second-Generation Wavelet (ARSGW) and the Hilbert transform, is proposed. The ARSGW is applied to reveal the transient components of the signal in time domain clearly. Then the Hilbert transform is used to extract fault features of rolling bearing from the wavelet packets. The analysis results of the vibration signals from the experiment and the machine tool spindle show that the proposed method can detect the faults of the rolling bearing effectively.

Online publication date: Wed, 30-Jul-2008

The full text of this article is only available to individual subscribers or to users at subscribing institutions.

 
Existing subscribers:
Go to Inderscience Online Journals to access the Full Text of this article.

Pay per view:
If you are not a subscriber and you just want to read the full contents of this article, buy online access here.

Complimentary Subscribers, Editors or Members of the Editorial Board of the International Journal of Materials and Product Technology (IJMPT):
Login with your Inderscience username and password:

Username:        Password:          Forgotten your password?

Want to subscribe?
A subscription gives you complete access to all articles in the current issue, as well as to all articles in the previous three years (where applicable). See our Orders page to subscribe.

If you still need assistance, please email subs@inderscience.com