Fault diagnosis and knowledge management of turbo-generator based on support vector machine

Author

Cai, Zhong-Jian ; Lu, Sheng ; Fengchuan, Z.

Author_Institution

Sch. of Comput. Sci. & Inf. Eng., Chongqing Technol. & Bus. Univ., Chongqing, China

fYear

2009

fDate

8-11 Aug. 2009

Firstpage

532

Lastpage

535

Abstract

Support vector machine (SVM) which overcomes the drawbacks of neural networks has been widely used for pattern recognition in recent years. In the study, the proposed SVM model is applied to fault diagnosis of turbo-generator, and the method of knowledge management in SVM diagnostic system of turbo-generator is presented. The real data sets are used to investigate its feasibility in fault diagnosis of turbo-generator. The experimental results show that SVM not only has high diagnostic accuracy, but also has excellent anti-noise capability.

Keywords

fault diagnosis; knowledge management; mechanical engineering computing; pattern classification; support vector machines; turbogenerators; antinoise capability; fault diagnosis; knowledge management; neural networks; pattern recognition; support vector machine; turbogenerator; Artificial neural networks; Computer science; Electronic mail; Fault diagnosis; Knowledge engineering; Knowledge management; Lagrangian functions; Pattern recognition; Support vector machine classification; Support vector machines;

fLanguage

English

Publisher

ieee

Conference_Titel

Computer Science and Information Technology, 2009. ICCSIT 2009. 2nd IEEE International Conference on

Conference_Location

Beijing

Print_ISBN

978-1-4244-4519-6

Electronic_ISBN

978-1-4244-4520-2

Type

conf

DOI

10.1109/ICCSIT.2009.5234891

Filename

5234891