Table of Contents Author Guidelines Submit a Manuscript
Shock and Vibration
Volume 2015, Article ID 847802, 8 pages
http://dx.doi.org/10.1155/2015/847802
Research Article

Application of LCD-SVD Technique and CRO-SVM Method to Fault Diagnosis for Roller Bearing

1College of Mechanical Engineering, Hunan University of Arts and Science, Changde 415003, China
2College of Mechanical and Vehicle Engineering, Hunan University, Changsha 410082, China
3Institute for Computational Science, Ton Duc Thang University, Ho Chi Minh 70000, Vietnam
4Faculty of Civil Engineering, Ton Duc Thang University, Ho Chi Minh 70000, Vietnam
5Faculty of Mechanical Engineering, Industrial University of Ho Chi Minh City, Ho Chi Minh 70000, Vietnam

Received 5 December 2014; Accepted 19 March 2015

Academic Editor: Anindya Ghoshal

Copyright © 2015 Songrong Luo et al. 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.

Abstract

Targeting the nonlinear and nonstationary characteristics of vibration signal from fault roller bearing and scarcity of fault samples, a novel method is presented and applied to roller bearing fault diagnosis in this paper. Firstly, the nonlinear and nonstationary vibration signal produced by local faults of roller bearing is decomposed into intrinsic scale components (ISCs) by using local characteristic-scale decomposition (LCD) method and initial feature vector matrices are obtained. Secondly, fault feature values are extracted by singular value decomposition (SVD) techniques to obtain singular values, while avoiding the selection of reconstruction parameters. Thirdly, a support vector machine (SVM) classifier based on Chemical Reaction Optimization (CRO) algorithm, called CRO-SVM method, is designed for classification of fault location. Lastly, the proposed method is validated by two experimental datasets. Experimental results show that the proposed method based LCD-SVD technique and CRO-SVM method have higher classification accuracy and shorter cost time than the comparative methods.