Paper Info
Title
Condition Assessment of Power Supply Equipment Based on Kernel Principal Component Analysis and Multi-class Support Vector Machine
Abstract
The power supply equipment is very complicated, which makes the process of assessment too long. The traditional method of assessment is also not comprehensive enough, which induces low accuracy of assessment. A method based on kernel principal component analysis and fast multi-class support vector machine is introduced in this paper: kernel principal component analysis, as the preprocessor of the index system, analyses the most important factors which influence equipment condition. Then multi-class support vector machine, as the assessment tool, can classify power supply equipments as per the requirements of condition based maintenance. The result of experiment shows that the method can reduce the complex of assessment and is more comprehensive. It also improves rapidity and accuracy of traditional assessment.
Year
DOI
Venue
2009
10.1109/ICNC.2009.433
ICNC (1)
Keywords
Field
DocType
traditional method,equipment condition,kernel principal component analysis,low accuracy,condition assessment,assessment tool,comprehensive enough,power supply equipments,power supply equipment,kernel principal,traditional assessment,multiclass support vector machine,component analysis,multi-class support,multi-class support vector machine,support vector machine,data mining,principal component analysis,kernel,indexes,maintenance engineering,support vector machines,indexation,testing
Kernel (linear algebra),Data mining,Condition-based maintenance,Computer science,Support vector machine,Kernel principal component analysis,Preprocessor,Condition monitoring,Artificial intelligence,Machine learning,Maintenance engineering,Principal component analysis
Conference
Citations 
PageRank 
References 
0
0.34
5
Authors
2
Name
Order
Citations
PageRank
SUN Wei124726.63
Guozhen Ma200.34