Paper Title:
KPCA Denoising and its Application in Machinery Fault Diagnosis
  Abstract

This paper proposes a kernel principal component analysis (KPCA)-based denoising method for removing the noise from vibration signal. Firstly, one-dimensional time series is expanded to multidimensional time series by the phase space reconstruction method. Then, KPCA is performed on the multidimensional time series. The first kernel principal component is the denoised signal. A rolling bearing denoising example verify the effectiveness of the proposed method

  Info
Periodical
Edited by
Qiancheng Zhao
Pages
274-278
DOI
10.4028/www.scientific.net/AMM.103.274
Citation
L. L. Jiang, Z. Q. Deng, S. W. Tang, "KPCA Denoising and its Application in Machinery Fault Diagnosis", Applied Mechanics and Materials, Vol. 103, pp. 274-278, 2012
Online since
September 2011
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