Paper Title:
Civil Aeroengine Fault Diagnosis Based on Fuzzy Least Square Support Vector Machine
  Abstract

SVM(Support Vector Machine) is a new artificial intelligence methodolgy, basing on structural risk mininization principle, which has better generalization than the traditional machine learning and SVM shows powerfulability in learning with limited samples. To solve the problem of lack of engine fault samples, FLS-SVM theory, an improved SVM, which is a method is applied. 10 common engine faults are trained and recognized in the paper.The simulated datas are generated from PW4000-94 engine influence coefficient matrix at cruise, and the results show that the diagnostic accuracy of FLS-SVM is better than LS-SVM.

  Info
Periodical
Edited by
Han Zhao
Pages
2047-2050
DOI
10.4028/www.scientific.net/AMM.130-134.2047
Citation
H. C. Qu, X. B. Ding, "Civil Aeroengine Fault Diagnosis Based on Fuzzy Least Square Support Vector Machine", Applied Mechanics and Materials, Vols. 130-134, pp. 2047-2050, 2012
Online since
October 2011
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