Paper Title:
The Research on the Method of Feature Selection in Support Vector Machine Based Entropy
  Abstract

The large rotating machinery functioning of the rotor is one of the most important issues. It has great significance to identify the fault early and implement intelligent fault diagnosis. However there is a big nonlinear about large rotating machinery and has less fault samples. This led great difficulties for feature selection and state recognition. Based on Entropy in feature selection, we extract each intrinsic mode’s function energy as eigenvector and make them for input parameter of the support vector machine (SVM) to fault diagnosis. The experiment shows that this method can classify the fault state, and completed intelligent fault diagnosis.

  Info
Periodical
Advanced Materials Research (Volumes 354-355)
Chapter
Chapter 7: High Voltage and Insulation Technology
Edited by
Hao Zhang, Yang Fu and Zhong Tang
Pages
1192-1196
DOI
10.4028/www.scientific.net/AMR.354-355.1192
Citation
X. Y. Zhu, X. Tian, X. X. Zhu, "The Research on the Method of Feature Selection in Support Vector Machine Based Entropy", Advanced Materials Research, Vols. 354-355, pp. 1192-1196, 2012
Online since
October 2011
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Price
$32.00
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