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].

• Periodical: Applied Mechanics and Materials (Volumes 48-49)
• Pages: 241-245
• DOI: https://doi.org/10.4028/www.scientific.net/AMM.48-49.241
• Online since: February 2011
• Authors: Hong Bing Gao, Liao Yang, Xian Zhang, Chen Cheng
• Keywords: Kernel Function, Rolling Bearing Fault, Support Vector Machine (SVM)

References

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