Title :
Study of fault diagnosis based on SVM for turbine generator unit
Author :
Chunmei Xu ; Hao Zhang ; Daogang Peng
Author_Institution :
Sch. of Electron. & Inf., Tongji Univ., Shanghai, China
Abstract :
A support vector machine (SVM) is presented for diagnosing the fault of the turbine generator unit. The SVM is based on the statistical learning theory and the structural risk minimization principle. It not only has greater generalization ability, but also a better solution to the small sample learning classification problems. In the case of limited feature information, SVM can explore furthest the classification of knowledge implicit in the sample data, and thus achieve better classification results. The simulation results show that the proposed method can effectively diagnose the vibration fault of turbine generator, and has good application prospects.
Keywords :
fault diagnosis; learning (artificial intelligence); mechanical engineering computing; pattern classification; risk analysis; statistical analysis; support vector machines; turbogenerators; vibrations; SVM; fault diagnosis; small sample learning classification problems; statistical learning theory; structural risk minimization principle; support vector machine; turbine generator unit; vibration fault; Educational institutions; Fault diagnosis; Generators; Kernel; Neural networks; Support vector machines; Turbines; Fault Diagnosis; Support Vector Machine; Turbine;
Conference_Titel :
Natural Computation (ICNC), 2012 Eighth International Conference on
Conference_Location :
Chongqing
Print_ISBN :
978-1-4577-2130-4
DOI :
10.1109/ICNC.2012.6234698