Title :
On Rotary Machine´s Multi-Class Fault Recognition Based on SVM
Author :
Xiaojun, Gu ; Shixi, Yang ; Suxiang, Qian
Author_Institution :
Zhejiang Univ., Hangzhou
Abstract :
In response to the lack of rotary mechanical diagnostic samples, this paper takes the advantages of support vector machine (SVM) in small sample classification for rotary machine multi-class fault pattern recognition, and introduces three methods based on binary classifications: "one-against-all", "one-against-one", and "directed acyclic graph" SVM (DAGSVM) and then compare their performance. The experiments indicate that the SVM has high adaptability for rotary machine fault diagnosis in the case of small number of samples.
Keywords :
directed graphs; fault diagnosis; machinery; mechanical engineering computing; pattern recognition; support vector machines; binary classification; directed acyclic graph SVM; fault pattern recognition; multiclass fault recognition; rotary machine; rotary mechanical diagnostic samples; small sample classification; support vector machine; Educational institutions; Fault diagnosis; Hydrogen; Mechanical engineering; Pattern recognition; Power engineering and energy; Support vector machine classification; Support vector machines; Virtual colonoscopy; Fault Diagnosis; Pattern Recognition; Rotary Machine; Support Vector Machine (SVM);
Conference_Titel :
Control Conference, 2007. CCC 2007. Chinese
Conference_Location :
Hunan
Print_ISBN :
978-7-81124-055-9
Electronic_ISBN :
978-7-900719-22-5
DOI :
10.1109/CHICC.2006.4347003
