Title :
Research on fault recognition method based on variable-risk SVM
Author :
Fuzhou Feng ; Aiwei Si ; Chaosheng Zhang
Author_Institution :
Dept. of Mech. Eng., Acad. of Armored Force Eng., Beijing, China
Abstract :
Because the tradition methods of fault pattern recognition can not distinguish the different loss by different misjudgments, the variable-risk support vector machines (SVM) is proposed in this paper. Then, the optimal classification face is redesigned and expert´s experience is integrated when using an actual data to recognize the fault, which makes the result more reliable. Finally, this method has already applied in the diesel engine fault diagnosis successfully.
Keywords :
diesel engines; fault diagnosis; pattern recognition; support vector machines; diesel engine fault diagnosis; fault pattern recognition; fault recognition method; optimal classification face; variable-risk SVM; variable-risk support vector machines; Diesel engines; Equations; Face; Fault diagnosis; Mathematical model; Pattern recognition; Support vector machines; fault recognition; support vector machines; vario-risk;
Conference_Titel :
Electronic and Mechanical Engineering and Information Technology (EMEIT), 2011 International Conference on
Conference_Location :
Harbin, Heilongjiang, China
Print_ISBN :
978-1-61284-087-1
DOI :
10.1109/EMEIT.2011.6023159
