Paper Title:
Identification of Natural Frequency of Bearing Rotor Based on GA-SVM
  Abstract

During identify natural frequency of bearing rotor, due to the complex non-linear relationship among the factors which influence natural frequency, so it is hard to establish a complete and accurate theoretical model. Based on the generalization and approximation of non-linear mapping capability of support vector machine (SVM) and the powerful ability of global optimization of the genetic algorithm (GA), the paper through optimizing the SVM by GA, establishes combined Genetic Support Vector Machine (GA-SVM). The method establishes the mapping between the natural frequency of a rolling bearing rotor and the various parameters, which reduces the rotor structure for the study similar to the natural frequency of the calculation of the workload greatly. Using the model to indentify the natural frequency of bearing rotor under different parameters, then compare identification value with experimental values shows that projections in good agreement with the experimental data.

  Info
Periodical
Advanced Materials Research (Volumes 443-444)
Chapter
Chapter 1: Advances in Manufacturing Engineering Techniques and Materials Science
Edited by
Li Jian
Pages
27-33
DOI
10.4028/www.scientific.net/AMR.443-444.27
Citation
T. R. Ma, F. H. Qin, R. X. Liu, F. J. Zhang, "Identification of Natural Frequency of Bearing Rotor Based on GA-SVM", Advanced Materials Research, Vols. 443-444, pp. 27-33, 2012
Online since
January 2012
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Price
$32.00
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