Title :
Research and Application of a Hierarchical Fault Diagnosis System Based on Support Vector Machine
Author :
Liu, Ailun ; Yuan, Xiaoyan ; Yu, Jinshou
Author_Institution :
East China Univ. of Sci. & Technol., Shanghai
Abstract :
support vector machine (SVM) is a kind of machine learning method based on the statistical learning theory, it has been applied in the fault diagnosis field. After analyzing SVM pattern classification theory, a hierarchical structure fault detection and identification (FDI) system is presented in this paper, and simulation results show that this method can effectively handle the complex process characteristic and improve FDI model performance.
Keywords :
fault diagnosis; learning (artificial intelligence); pattern classification; support vector machines; fault detection system; fault identification system; hierarchical fault diagnosis system; hierarchical structure; machine learning; pattern classification theory; statistical learning theory; support vector machine; Analytical models; Fault detection; Fault diagnosis; Learning systems; Pattern analysis; Pattern classification; Performance analysis; Statistical learning; Support vector machine classification; Support vector machines; Support Vector Machine (SVM); diagnosis; fault; pattern recognition;
Conference_Titel :
Natural Computation, 2007. ICNC 2007. Third International Conference on
Conference_Location :
Haikou
Print_ISBN :
978-0-7695-2875-5
DOI :
10.1109/ICNC.2007.607
