Shock and Vibration

Shock and Vibration / 2013 / Article

Open Access

Volume 20 |Article ID 241937 | https://doi.org/10.3233/SAV-2012-00739

W.Y. Liu, J.G. Han, "Rolling Element Bearing Fault Recognition Approach Based on Fuzzy Clustering Bispectrum Estimation", Shock and Vibration, vol. 20, Article ID 241937, 13 pages, 2013. https://doi.org/10.3233/SAV-2012-00739

Rolling Element Bearing Fault Recognition Approach Based on Fuzzy Clustering Bispectrum Estimation

Received26 Jun 2011
Revised15 Feb 2012

Abstract

A rolling element bearing fault recognition approach is proposed in this paper. This method combines the basic Higher-order spectrum (HOS) theory and fuzzy clustering method in data mining area. In the first step, all the bispectrum estimation results of the training samples and test samples are turned into binary feature images. Secondly, the binary feature images of the training samples are used to construct object templates including kernel images and domain images. Every fault category has one object templates. At last, by calculating the distances between test samples' binary feature images and the different object templates, the object classification and pattern recognition can be effectively accomplished. Bearing is the most important and much easier to be damaged component in rotating machinery. Furthermore, there exist large amounts of noise jamming and nonlinear coupling components in bearing vibration signals. The Higher Order Cumulants (HOC), which can quantitatively describe the nonlinear characteristic signals with close relationship between the mechanical faults, is introduced in this paper to de-noise the raw bearing vibration signals and obtain the bispectrum estimation pictures. In the experimental part, the rolling bearing fault diagnosis experiment results proved that the classification was completely correct.

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.


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