Simulation study on sensor fault diagnoses of the temperature of the boiler high-temperature part metal wall

Author

Dong, Ze ; Han, Pu ; Yin, Xi-Chao

Author_Institution

Dept. of Autom., North China Electr. Power Univ., Hebei, China

Volume

5

fYear

2004

fDate

26-29 Aug. 2004

Firstpage

3003

Abstract

A sensor fault diagnosis system is designed using SVR. SVR is trained off line, and used online. After being trained, SVR is used to simulate system dynamic characteristic. The simulation result is compared with actual output, and then fault error is drawn. Then use the categorized algorithm of fault based on the SVR of the decision tree, classify the faults. The simulation result shows that, SVR can simulate the system more accurately, thus the fault error is very precise and the classification to fault is correct. This assures the validity of this fault diagnosis system.

Keywords

boilers; decision trees; fault diagnosis; pattern classification; regression analysis; support vector machines; temperature sensors; walls; boiler wall; decision tree; high temperature metal wall; pattern classification; sensor fault diagnosis system; support vector machine training; support vector regression; temperature sensor; Boilers; Fault diagnosis; Learning systems; Machine learning; Machine learning algorithms; Power generation; Sensor arrays; Support vector machine classification; Support vector machines; Temperature sensors;

fLanguage

English

Publisher

ieee

Conference_Titel

Machine Learning and Cybernetics, 2004. Proceedings of 2004 International Conference on

Print_ISBN

0-7803-8403-2

Type

conf

DOI

10.1109/ICMLC.2004.1378547

Filename

1378547