Bearing Binary Classification Intelligent Diagnosis by Combined Improved EEMD with SVM

Abstract:

In order to perform the bearing intelligent fault diagnosis,combined improved EEMD with SVM respectively applied to the binary classification identification of bearing normal and ball fault, normal and inner circle fault,normal and outer ring fault in this paper.Improve EEMD decomposed 9d normalized energy for characteristic vector,the SVM binary classification and recognition of bearings normal and ball fault, normal and inner circle fault, normal and outer ring fault is researched.Compared to the SVM classification accuracy using different kernel functions that is linear kernel function, polynomial kernel function, RBF kernel function and Sigmoid kernel function.In the same parameters,SVM classification accuracy based on linear kernel function and polynomial kernel function is a hundred percent.Bearing normal and ball fault,normal and inner circle fault,normal and outer ring fault is completely correct apart.And there are the classification errors based on RBF kernel function and Sigmoid kernel functions.

• Periodical: Applied Mechanics and Materials (Volumes 341-342)
• Pages: 1066-1070
• DOI: https://doi.org/10.4028/www.scientific.net/AMM.341-342.1066
• Online since: July 2013
• Authors: Mei Jun Zhang, Jie Huang, Kai Chai, Hao Chen
• Keywords: Classification, EEMD, Identification, Intelligent Diagnosis, Support Vector Machine (SVM)

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