Application of Principal Components Analysis in Condenser Fault Diagnosis

Author

Han, Xiaojuan ; Xu, Daping ; Liu, Yibing

Author_Institution

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

Volume

2

fYear

0

fDate

0-0 0

Firstpage

5666

Lastpage

5669

Abstract

Condenser faults are usually caused by a variety of factors. The symptoms of different faults are characterized by their similarities. The relevance of such similar symptoms produces difficulties of data analysis. The parameters that impact condenser faults are comprehensively optimized by using principal components analysis (PCA) in this paper so that the information of feature parameters can be fused to several principal components, and then the dimensions of standard fault patterns can be determined according to the contribution ratios of each principal component, it is proved that the method is valid by cluster analysis for the samples to be diagnosed

Keywords

condensers (steam plant); data analysis; fault diagnosis; power system faults; principal component analysis; cluster analysis; condenser fault diagnosis; data analysis; fault pattern; principal component analysis; Data analysis; Data security; Fault diagnosis; Feature extraction; Information analysis; Optimization methods; Pattern analysis; Principal component analysis; Sensor phenomena and characterization; Statistical analysis; condenser; data fusion; fault diagnosis; primary component analysis;

fLanguage

English

Publisher

ieee

Conference_Titel

Intelligent Control and Automation, 2006. WCICA 2006. The Sixth World Congress on

Conference_Location

Dalian

Print_ISBN

1-4244-0332-4

Type

conf

DOI

10.1109/WCICA.2006.1714160

Filename

1714160