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
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;
Conference_Titel :
Intelligent Systems, 2009. GCIS '09. WRI Global Congress on
Conference_Location :
Xiamen
Print_ISBN :
978-0-7695-3571-5
DOI :
10.1109/GCIS.2009.312
