DocumentCode
498281
Title
Fault Diagnosis Based on Support Vector Machines and Convex Hulls
Author
Li, Han ; Yuan-Yuan, Zheng ; Ru-Yi, Dong
Author_Institution
Sch. of Autom. Eng., Northeast Dianli Univ., Jilin, China
Volume
3
fYear
2009
fDate
19-21 May 2009
Firstpage
534
Lastpage
538
Abstract
Through the analysis of support vector machines principle, the theory of convex hull based on support vector machines is used for the fault diagnosis, with acme-set of convex hull replacing the entire sample set to train, then, use actual data to do the simulation. The simulation results show that this method has the same performance as learning by the entire sample set, but reduces the storage space and improves the learning speed.
Keywords
computational geometry; electric machine analysis computing; fault diagnosis; learning (artificial intelligence); pattern classification; support vector machines; turbogenerators; convex hull; fault diagnosis; machine learning; pattern classification; support vector machine; turbogenerator unit; Automation; Cities and towns; Data engineering; Fault diagnosis; Intelligent systems; Production; Static VAr compensators; Statistical learning; Support vector machine classification; Support vector machines; convex hull; fault diagnosis; support vector machines;
fLanguage
English
Publisher
ieee
Conference_Titel
Intelligent Systems, 2009. GCIS '09. WRI Global Congress on
Conference_Location
Xiamen
Print_ISBN
978-0-7695-3571-5
Type
conf
DOI
10.1109/GCIS.2009.312
Filename
5209107
Link To Document