Civil Aeroengine Fault Diagnosis Based on Fuzzy Least Square Support Vector Machine

Hong Chun Qu; Xie Bin Ding

doi:10.4028/www.scientific.net/AMM.130-134.2047

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HomeApplied Mechanics and MaterialsApplied Mechanics and Materials Vols. 130-134Civil Aeroengine Fault Diagnosis Based on Fuzzy...

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.

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Periodical:

Applied Mechanics and Materials (Volumes 130-134)

Pages:

2047-2050

DOI:

https://doi.org/10.4028/www.scientific.net/AMM.130-134.2047

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Online since:

October 2011

Authors:

Hong Chun Qu, Xie Bin Ding

Keywords:

Aerospace Propulsion System, Artificial Intelligence (AI), Fault Diagnosis, FLS-SVM

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