Shock and Vibration

Shock and Vibration / 2009 / Article

Open Access

Volume 16 |Article ID 519502 | https://doi.org/10.3233/SAV-2009-0457

Junsheng Cheng, Dejie Yu, Jiashi Tang, Yu Yang, "Application of SVM and SVD Technique Based on EMD to the Fault Diagnosis of the Rotating Machinery", Shock and Vibration, vol. 16, Article ID 519502, 10 pages, 2009. https://doi.org/10.3233/SAV-2009-0457

Application of SVM and SVD Technique Based on EMD to the Fault Diagnosis of the Rotating Machinery

Received07 Dec 2006
Revised29 Feb 2008

Abstract

Targeting the characteristics that periodic impulses usually occur whilst the rotating machinery exhibits local faults and the limitations of singular value decomposition (SVD) techniques, the SVD technique based on empirical mode decomposition (EMD) is applied to the fault feature extraction of the rotating machinery vibration signals. The EMD method is used to decompose the vibration signal into a number of intrinsic mode functions (IMFs) by which the initial feature vector matrices could be formed automatically. By applying the SVD technique to the initial feature vector matrices, the singular values of matrices could be obtained, which could be used as the fault feature vectors of support vector machines (SVMs) classifier. The analysis results from the gear and roller bearing vibration signals show that the fault diagnosis method based on EMD, SVD and SVM can extract fault features effectively and classify working conditions and fault patterns of gears and roller bearings accurately even when the number of samples is small.

Copyright © 2009 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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