A steam turbine-generator vibration fault diagnosis method based on rough set

Author

Jian, Ou ; Cai-Xin, Sun ; Weimin, Bi ; Bide, Zhang ; Ruijin, Liao

Author_Institution

Lab. of High Voltage Eng. & Electr. New Technol. of Minist. of Educ., Chongqing Univ., China

Volume

3

fYear

2002

fDate

2002

Firstpage

1532

Abstract

According to turbine-generator vibration characteristic spectrum, a discretized generator fault attribute decision table and condition. attribute set reduction method based on rough set theory are presented in this paper, though the key character which influences classifying is picked up. BP network input dimension is reduced and training time is saved. Experiment shows that the result is effective.

Keywords

backpropagation; fault diagnosis; neural nets; power engineering computing; rough set theory; steam turbines; turbogenerators; vibration measurement; BP network input dimension reduction; condition attribute set reduction method; discretized generator fault attribute decision table; neural networks; rough set theory; steam turbine-generator; training time reduction; vibration fault diagnosis method; Bismuth; Character generation; Fault diagnosis; Information systems; Machine learning; Neural networks; Rough sets; Set theory; Sun; Turbines;

fLanguage

English

Publisher

ieee

Conference_Titel

Power System Technology, 2002. Proceedings. PowerCon 2002. International Conference on

Print_ISBN

0-7803-7459-2

Type

conf

DOI

10.1109/ICPST.2002.1067789

Filename

1067789