Modified DWT for Feature Extraction of Bear Failure Vibration Signal

Junjiang Zhu*, Lingsong He
Department of Mechanical Science and Engineering, Huazhong University of Science and Technology, Wuhan, Hubei, 430074, China

© 2015 Zhu and He

open-access license: This is an open access article distributed under the terms of the Creative Commons Attribution 4.0 International Public License (CC-BY 4.0), a copy of which is available at: ( This license permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.


In this paper, bear fault is automatically diagnosed by using pattern recognition. To improve the resolution of lower frequency part, we introduce scale factors to discrete wavelet composition (DWT). The modified DWT combined with high order cumulates are used for vibration signal feature extraction. Besides we use principle component analysis to reduce dimension of the feature data. This feature extraction method has a lower dimension and a higher resolution for lower frequency parts. Therefore it can not only reveal the characteristics of non-linear relationship between amounts of features, but also help to improve the speed and accuracy of classification. Finally neural network algorithm is used for fault classification. Result shows that our method can accurately and efficiently identify the type of bearing failures.

Keywords: Nonlinear vibration signal, neural network, PCA, wavelet.