Fault Diagnosis of Roller Bearing Using Dual-Tree Complex Wavelet Transform, Rough Set and Neural Network

DOI

10.2991/iccsee.2013.301How to use a DOI?

Keywords

Dual-Tree Complex Wavelet Transform, rough set theory, Neural Network, rolling element bearings, fault diagnosis

Abstract

In a complex field environment for modern mechanical equipment, how to identify all kinds of operational status of the rolling element bearings fastly and accurately is very important and necessary. A novel approach to automated diagnosis is introduced, which is based on feature extraction with the Dual-Tree Complex Wavelet Transform (DT-CWT), then attribute reduction with rough set theory and finally pattern recognition with Artificial Neural Network. In our experiment, 4 kinds of states on a rolling element bearing test table, including normal, pitting on inner ring, pitting on outer ring and pitting on rolling element, are adopted. The experimental results indicate that the proposed feature extraction and automated diagnosis method can extract significant feature sets from signal, and can accurately distinguish many fault pattern, and has some practical value for the on-line condition monitoring of modern industrial demands.

Copyright

© 2013, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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TY  - CONF
AU  - Zhixin Chen
AU  - Lixin Gao
PY  - 2013/03
DA  - 2013/03
TI  - Fault Diagnosis of Roller Bearing Using Dual-Tree Complex Wavelet Transform, Rough Set and Neural Network
BT  - Proceedings of the 2nd International Conference on Computer Science and Electronics Engineering (ICCSEE 2013)
PB  - Atlantis Press
SP  - 1196
EP  - 1199
SN  - 1951-6851
UR  - https://doi.org/10.2991/iccsee.2013.301
DO  - 10.2991/iccsee.2013.301
ID  - Chen2013/03
ER  -