Title :
Study on the fault of power-shift steering transmission based on SVM structural risk
Author :
Zhang, Ying-Feng ; Ma, Biao ; Zheng, Chang-Song ; Zhang, Jin-Le
Abstract :
Support vector machine (SVM) is an efficient method for data mining of oil analysis. The principle and structural risk of SVM are described in this paper. And the structural risk is studied using oil analysis data. During the process, parameters determination is a very important part because parameters have great influence on the performance of SVM. We select the Radial Basis Function (RBF) as the kernel function of SVM and study the influence of parameters s and C for SVM structural risk. The recognition rate of SVM model is influenced by SVM structural risk. The recognition rate is studied through an experimentation research.
Keywords :
condition monitoring; data mining; fault diagnosis; mechanical engineering computing; oils; pattern recognition; power transmission (mechanical); radial basis function networks; risk management; steering systems; support vector machines; data mining; kernel function; oil analysis; parameters determination; power-shift steering transmission; radial basis function; recognition rate; structural risk; support vector machine; Automation; Data analysis; Data mining; Fault diagnosis; Kernel; Petroleum; Power engineering and energy; Risk analysis; Support vector machine classification; Support vector machines; Power-Shift Steering Transmission; SVM; Structural Risk;
Conference_Titel :
Mechatronics and Automation, 2009. ICMA 2009. International Conference on
Conference_Location :
Changchun
Print_ISBN :
978-1-4244-2692-8
Electronic_ISBN :
978-1-4244-2693-5
DOI :
10.1109/ICMA.2009.5246092
