Prediction of Aero Engine Fault by Relative Vector Machine and Genetic Algorithm Model

Jian Wang; Xiao Hu Duan; Yan Li; Peng Bai

doi:10.4028/www.scientific.net/AMR.998-999.1033

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HomeAdvanced Materials ResearchAdvanced Materials Research Vols. 998-999Prediction of Aero Engine Fault by Relative Vector...

Prediction of Aero Engine Fault by Relative Vector Machine and Genetic Algorithm Model

Abstract:

Diagnosis of engine fault is critical in reducing maintenance costs. A new method which incorporates hybrid relative vector machines and genetic algorithm (RVM-GA) was proposed to predict aero engine fault based on data of the spectrometric oil analysis. Experimental results show that it has a high accuracy and effective properties.

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Periodical:

Advanced Materials Research (Volumes 998-999)

Pages:

1033-1036

DOI:

https://doi.org/10.4028/www.scientific.net/AMR.998-999.1033

Citation:

Cite this paper

Online since:

July 2014

Authors:

Jian Wang*, Xiao Hu Duan, Yan Li, Peng Bai

Keywords:

Aero Engine, Fault, GA, RVM, Wear Debris of Oil

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* - Corresponding Author

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