The Application of ART2 Two-Factor Optimization Method to Engine Fault Diagnosis

Author

Sun, Ye ; Wang, Hongxin ; Guo, Xiang ; Yi, Zhejing

Author_Institution

Wuhan Mech. Technol. Coll., Wuhan, China

Volume

1

fYear

2011

fDate

24-25 Sept. 2011

Firstpage

18

Lastpage

21

Abstract

In order to make ART2 network in engine fault diagnosis of the unknown fault. The vigilance factor and the adjustment factor were proposed to control the re-learning of the known fault. Through judging the credibility of the sample and amending the study mode, adjust the top-down and bottom-up vector. The analysis shows that the two-factor method solved the problem of the lack of training samples and overcomed the disadvantage of the traditional neural network that it was unable to identify the defects. This method can assure the network to continue self-learning and optimization by effectively classifying and identifying the state model of the engine.

Keywords

engines; fault diagnosis; learning (artificial intelligence); mechanical engineering computing; neural nets; vectors; ART2 two-factor optimization method; adjustment factor; bottom-up vector; engine fault diagnosis; neural network; sample credibility; self-learning; state model classification; top-down vector; vigilance factor; Adaptation models; Biological neural networks; Engines; Neurons; Support vector machine classification; Training; Vectors; ART2; Fault diagnosis; Neural network; Recognition algorithm; Two-factor method;

fLanguage

English

Publisher

ieee

Conference_Titel

Information Technology, Computer Engineering and Management Sciences (ICM), 2011 International Conference on

Conference_Location

Nanjing, Jiangsu

Print_ISBN

978-1-4577-1419-1

Type

conf

DOI

10.1109/ICM.2011.17

Filename

6113345