Paper Title:
Application and Experimental Study of Support Vector Machine in Rolling Bearing Fault
  Abstract

A brief introduction of the basic concepts of the classification interval, the optimal classification surface and support vector; explained derivation of SVM based on Lagrange optimization method; Sigmoid kernel function and so on. It describes three methods of C-SVM、V-SVM and least squares SVM based on Sigmoid kernel function. To a bearing failure as a example to compare three results of SVM training of the kernel linear function, polynomial kernel function, Sigmoid kernel function, The results show that satisfactory fault analysis demand the appropriate kernel function selection. Fault in the gear box, the bearing failure is 19%, In addition, the rate is as high as 30% in other rotating machinery system failure [1,2].Thus, rolling bearing condition monitoring and fault diagnosis are very important to production safety, and many scholars have done numerous studies [3,4]. Support vector machine method is a learning methods based on statistical learning theory Vapnik-Chervonenkis dimension theory and structural risk minimization [5,6].

  Info
Periodical
Edited by
Zhixiang Hou
Pages
241-245
DOI
10.4028/www.scientific.net/AMM.48-49.241
Citation
H. B. Gao, L. Yang, X. Zhang, C. Cheng, "Application and Experimental Study of Support Vector Machine in Rolling Bearing Fault", Applied Mechanics and Materials, Vols. 48-49, pp. 241-245, 2011
Online since
February 2011
Export
Price
$32.00
Share

In order to see related information, you need to Login.

In order to see related information, you need to Login.

Authors: Bin Ren, Liang Lun Cheng
Abstract:Polynomial smooth techniques are applied to Support Vector Regression model by an accurate smooth approximation which is offered by Hermite...
2215
Authors: Hong Xia Zhao, Zhi Xia Liu, Zhi Yang Luo, Guan Yun Xiao
Abstract:The color of farm produce is a very important index of quality, its nutrition is correlative with itself color. At present, most of the...
268
Authors: Qun Cao, Bing Xiang Liu, Xiang Chen
Chapter 1: Development and Utilization of Solar Energy
Abstract:According to the nonlinearity and uncertainty of the water quality data samples, a forecasting model based on Simulated Annealing Genetic...
781
Authors: Jian Qiong Xiao
Chapter 1: Numbers, Intelligence
Abstract:Based on the foundation of prediction of networks security situation models, this article proposed a method about applying wavelet kernel...
53
Authors: Lv Zhao, Yi Dan Su, Hua Qin, Pian Pian Ma
Chapter 10: Intelligence Algorithm, Optimization Algorithm and their Applications
Abstract:The relevance vector machine (RVM) was a Bayesian framework for learning sparse regression models and classifiers, it used single kernel...
1308